This document is an introduction to topological data analysis (TDA) presented by Rodrigo Rojas Moraleda. It discusses the principles of TDA, including that the shape of data matters and can be measured topologically by counting loops and clusters rather than with numbers. Topology provides a way to represent shapes with less information, like nodes capturing a space's structure, rather than infinite point lists. The introduction covers what topology is, elements like manifolds and simplicial complexes, and outlines subsequent parts on homology, persistent homology, and applications to point clouds.
Semi-Supervised Discriminant Analysis Based On Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Balistrocchi, M., Metulini, R., Carpita, M., and Ranzi, R.: Dynamic maps of human exposure to floods based on mobile phone data, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-201, in press, 2020
A hybrid approach for analysis of dynamic changes in spatial dataijdms
Any geographic location undergoes changes over a period of time. These changes can be observed by
naked eye, only if they are huge in number spread over a small area. However, when the changes are small
and spread over a large area, it is very difficult to observe or extract the changes. Presently, there are few
methods available for tackling these types of problems, such as GRID, DBSCAN etc. However, these
existing mechanisms are not adequate for finding an accurate changes or observation which is essential
with respect to most important geometrical changes such as deforestations and land grabbing etc.,. This
paper proposes new mechanism to solve the above problem. In this proposed method, spatial image
changes are compared over a period of time taken by the satellite. Partitioning the satellite image in to
grids, employed in the proposed hybrid method, provides finer details of the image which are responsible
for improving the precision of clustering compared to whole image manipulation, used in DBSCAN, at a
time .The simplicity of DBSCAN explored while processing portioned grid portion.
MODELLING DYNAMIC PATTERNS USING MOBILE DATAcscpconf
Understanding, modeling and simulating human mobility among urban regions is very challengeable effort. It is very important in rescue situations for many kinds of events, either in the indoor events like evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency situations there are several incidents could be happened, the overcrowding causes injuries and death cases, which are emerged during emergency situations, as well as it serves urban planning and smart cities. The aim of this study is to explore the characteristics of human mobility patterns, and model themmathematically depending on inter-event time and traveled distances (displacements) parameters by using CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical simulation endorse the other studies findings in that the most of real systems patterns are almost follows an exponential distribution. In the future the mobility patterns could be classified according (work or off) days, and the radius of gyration could be considered as effective parameter in modelling human mobility.
Semi-Supervised Discriminant Analysis Based On Data Structureiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Balistrocchi, M., Metulini, R., Carpita, M., and Ranzi, R.: Dynamic maps of human exposure to floods based on mobile phone data, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2020-201, in press, 2020
A hybrid approach for analysis of dynamic changes in spatial dataijdms
Any geographic location undergoes changes over a period of time. These changes can be observed by
naked eye, only if they are huge in number spread over a small area. However, when the changes are small
and spread over a large area, it is very difficult to observe or extract the changes. Presently, there are few
methods available for tackling these types of problems, such as GRID, DBSCAN etc. However, these
existing mechanisms are not adequate for finding an accurate changes or observation which is essential
with respect to most important geometrical changes such as deforestations and land grabbing etc.,. This
paper proposes new mechanism to solve the above problem. In this proposed method, spatial image
changes are compared over a period of time taken by the satellite. Partitioning the satellite image in to
grids, employed in the proposed hybrid method, provides finer details of the image which are responsible
for improving the precision of clustering compared to whole image manipulation, used in DBSCAN, at a
time .The simplicity of DBSCAN explored while processing portioned grid portion.
MODELLING DYNAMIC PATTERNS USING MOBILE DATAcscpconf
Understanding, modeling and simulating human mobility among urban regions is very challengeable effort. It is very important in rescue situations for many kinds of events, either in the indoor events like evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency situations there are several incidents could be happened, the overcrowding causes injuries and death cases, which are emerged during emergency situations, as well as it serves urban planning and smart cities. The aim of this study is to explore the characteristics of human mobility patterns, and model themmathematically depending on inter-event time and traveled distances (displacements) parameters by using CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical simulation endorse the other studies findings in that the most of real systems patterns are almost follows an exponential distribution. In the future the mobility patterns could be classified according (work or off) days, and the radius of gyration could be considered as effective parameter in modelling human mobility.
Modelling dynamic patterns using mobile datacsandit
Understanding, modeling and simulating human mobility among urban regions is very challengeable
effort. It is very important in rescue situations for many kinds of events, either in the indoor events like
evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency
situations there are several incidents could be happened, the overcrowding causes injuries and death
cases, which are emerged during emergency situations, as well as it serves urban planning and smart
cities. The aim of this study is to explore the characteristics of human mobility patterns, and model them
mathematically depending on inter-event time and traveled distances (displacements) parameters by using
CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical
simulation endorse the other studies findings in that the most of real systems patterns are almost follows an
exponential distribution. In the future the mobility patterns could be classified according (work or off)
days, and the radius of gyration could be considered as effective parameter in modelling human mobility
Individual movements and geographical data mining. Clustering algorithms for ...Beniamino Murgante
Individual movements and geographical data mining. Clustering algorithms for highlighting hotspots in personal navigation routes.
Giuseppe Borruso, Gabriella Schoier - University of Trieste
GIS and Remote Sensing Training at Pitney Bowes SoftwareNishant Sinha
This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.
The aim of this project is to discover the topics of scientific papers published by researches of DEMS (Department of Economics, Management and Statistic) for the University of Milano-Bicocca.
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Accurate time series classification using shapeletsIJDKP
Time series data are sequences of values measured over time. One of the most recent approaches to
classification of time series data is to find shapelets within a data set. Time series shapelets are time series
subsequences which represent a class. In order to compare two time series sequences, existing work uses
Euclidean distance measure. The problem with Euclidean distance is that it requires data to be
standardized if scales differ. In this paper, we perform classification of time series data using time series
shapelets and used Mahalanobis distance measure. The Mahalanobis distance is a descriptive statistic
that provides a relative measure of a data point's distance (residual) from a common point. The
Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one. It
differs from Euclidean distance in that it takes into account the correlations of the data set and is scaleinvariant.
We show that Mahalanobis distance results in more accuracy than Euclidean distance measure
Sustainable Development Formal Definition and ModelingSSA KPI
AACIMP 2010 Summer School lecture by Alexander Makarenko. "Applied Mathematics" stream. "General Tasks and Problems of Modelling of Social Systems. Problems and Models in Sustainable Development" course. Part 9.
More info at http://summerschool.ssa.org.ua
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Modelling dynamic patterns using mobile datacsandit
Understanding, modeling and simulating human mobility among urban regions is very challengeable
effort. It is very important in rescue situations for many kinds of events, either in the indoor events like
evacuation of buildings or outdoor ones like public assemblies, community evacuation, in exigency
situations there are several incidents could be happened, the overcrowding causes injuries and death
cases, which are emerged during emergency situations, as well as it serves urban planning and smart
cities. The aim of this study is to explore the characteristics of human mobility patterns, and model them
mathematically depending on inter-event time and traveled distances (displacements) parameters by using
CDRs (Call Detailed Records) during Armada festival in France. However, the results of the numerical
simulation endorse the other studies findings in that the most of real systems patterns are almost follows an
exponential distribution. In the future the mobility patterns could be classified according (work or off)
days, and the radius of gyration could be considered as effective parameter in modelling human mobility
Individual movements and geographical data mining. Clustering algorithms for ...Beniamino Murgante
Individual movements and geographical data mining. Clustering algorithms for highlighting hotspots in personal navigation routes.
Giuseppe Borruso, Gabriella Schoier - University of Trieste
GIS and Remote Sensing Training at Pitney Bowes SoftwareNishant Sinha
This presentation was made for internal training in Pitney Bowes. The content has many references across and has not been compiled. A simple ppt will help a lot.
The aim of this project is to discover the topics of scientific papers published by researches of DEMS (Department of Economics, Management and Statistic) for the University of Milano-Bicocca.
- What is Clustering, Honeypots and Density Based Clustering?
- What is Optics Clustering and how is it different than DB Clustering? …and how
can it be used for outlier detection.
- What is so-called soft clustering and how is it different than clustering? …and how
can it be used for outlier detection.
Data Tactics Data Science Brown Bag (April 2014)Rich Heimann
This is a presentation we perform internally every quarter as part of our Data Science Brown Bag Series. This presentation was talking about different types of soft clustering techniques - all of which the team currently performs depending on the complexity of the data and the complexity of customer problems. If you are interested in learning more about working with L-3 Data Tactics or interested in working for the L-3 Data Tactics Data Science team please contact us soon! Thank you.
Accurate time series classification using shapeletsIJDKP
Time series data are sequences of values measured over time. One of the most recent approaches to
classification of time series data is to find shapelets within a data set. Time series shapelets are time series
subsequences which represent a class. In order to compare two time series sequences, existing work uses
Euclidean distance measure. The problem with Euclidean distance is that it requires data to be
standardized if scales differ. In this paper, we perform classification of time series data using time series
shapelets and used Mahalanobis distance measure. The Mahalanobis distance is a descriptive statistic
that provides a relative measure of a data point's distance (residual) from a common point. The
Mahalanobis distance is used to identify and gauge similarity of an unknown sample set to a known one. It
differs from Euclidean distance in that it takes into account the correlations of the data set and is scaleinvariant.
We show that Mahalanobis distance results in more accuracy than Euclidean distance measure
Sustainable Development Formal Definition and ModelingSSA KPI
AACIMP 2010 Summer School lecture by Alexander Makarenko. "Applied Mathematics" stream. "General Tasks and Problems of Modelling of Social Systems. Problems and Models in Sustainable Development" course. Part 9.
More info at http://summerschool.ssa.org.ua
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
1. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Introduction to Topological Data Analysis) (TDA)
Part 1 - Introduction
Rodrigo Rojas Moraleda
April 22 2012
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 1/31
2. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Outline
1 About
2 Introduction
3 Elements of Topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 2/31
3. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
1 About
2 Introduction
3 Elements of Topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 3/31
4. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
About
Image Processing Group
Luis Salinas Carrasco Dr.
rer. Nat (Mathematics) Professor at Department of Computer Science UTFSM. Electronic
Engineer, Areas of Interest: Computer Science and Computational Methods in Engineering, Functions Theory,
Theoretical Computer Science and Mathematical Foundations of Computer Science, Signal and Image Processing
Steffen H¨artel Dr. rer. Nat. (Biophysics) Group
leader of Laboratory of Scientific Image Processing (SCIAN-Lab) at the Medical Faculty of the University of Chile,
Santiago, ChileAreas of Interest: to develop mathematical tools and computational algorithms to access dynamic,
morphologic, and topologic features in experimental systems with a biophysical, biological, or medical background
Rodrigo Rojas Moraleda
Ph.D student in Computer Science, Engineer
in Computer Science, B.Sc. Engineering minor
in Computer Science. Areas of Interest: Image
Processing, Computer Vision, Data Analysis, Biomedical applications
Raquel Pezoa Rivera Ph.D
student in Computer Science, M.Sc in Computer
Science, Universidad T´ecnica Federico Santa Mar´ıa
(UTFSM) Computer Science Engineer, UTFSM
Paola Arce Ph.D student
in Computer Science. Time series data analysis
Cesar Fernandez Ph.D student
in Computer Science. Time series data analysis
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 4/31
5. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Summary of contents
Part 1 - Introduction
Part 1 - Introduction
Introduction & Motivation
What is topology
What is computational topology.
Elements of Topology.
Manifolds.
Topological spaces.
Introduction to simplicial
complexes.
Geometrical Simplicial
Complexes.
Abstract Simplicial
Complexes
Part 2 - Homology
What is homology
Elements of homology by example.
Chains.
Clyces.
Boundary operator.
Homology classes.
Homology groups.
Betty numbers.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 5/31
6. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Simplicial Homology
Part 3 - Simplicial Homology
Point Clouds
Homotopy & Nervs
Alpha complex
Rips Complex
Cech Complex
Sandwich theorem
Part 4 - Persistence homology
Concepts
Filtration
Barcodes
Uses and applications
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 6/31
7. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
1 About
2 Introduction
3 Elements of Topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 7/31
8. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Priciple
Shape of data matters
Statistically a crudeaspect is how many connected
componnets can break into. clusters groups content
part of the phenomena.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 8/31
9. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
What is shape of data ?
Formaly defined in terms of a distance funtion of a metric e.g.
correlation distances
binary data Hamming distances
Euclidean distances
alphabet letter distances in Genes sequences.
Encoding notion of similarity o disimimilarity.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 9/31
10. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
What is shape of data ?
Formaly defined in terms of a distance funtion of a metric e.g.
correlation distances
binary data Hamming distances
Euclidean distances
alphabet letter distances in Genes sequences.
Encoding notion of similarity o disimimilarity.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 9/31
11. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
We trust in small distances
Physics define a theoretical
justify based on notion for
distances or meassurements
In life or social sciences,
distance (metric) are
constructed using a notion of
similarity (proximity).
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 10/31
12. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
We trust in small distances
1.8
1.5
Both pairs are regarded as similar, but the strength of the similarity as
encoded by the distance may not be so significant
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 11/31
13. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
We trust in small distances?
Even Local Connections are Noisy, depending on observer’s scale!
15
10
5
0
-5
-10
-15
-15 -15 -10 -5 0 10 15
Is it a circle, dots, or circle of circles?
To see the circle, we ignore variations in small distance (tolerance for proximity)
Because distance measurements are noisy Similar objects lie in
neighborhood of each other, which suffices to define topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 12/31
14. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
We trust in small distances?
Even Local Connections are Noisy, depending on observer’s scale!
15
10
5
0
-5
-10
-15
-15 -15 -10 -5 0 10 15
Is it a circle, dots, or circle of circles?
To see the circle, we ignore variations in small distance (tolerance for proximity)
Because distance measurements are noisy Similar objects lie in
neighborhood of each other, which suffices to define topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 12/31
15. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
16. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
17. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
18. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
19. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
20. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
21. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
22. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
23. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
24. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
according to Gunnar Carlsson
Mathematical formalism for doing two things:
◦ Meassuring shapes
· what is meassuring a shape?.
· counting loops, clusters?
· is not something that can be done easily in
terms of a number.
· is a vague notion.
◦ Representing shapes
· shape in a metric spaces is a infinite list of
points and distances.
· we would like a way to representing and
understanding the shape in a musch smaller
amount of information like a triangulation or
set of nodes that capture the structure of the
space much simpler representation.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 13/31
25. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is topology?
This is the subject of topology
Origins of Topology in Math
Leonhard Euler 1736, Seven Bridges of K¨onigsberg
Johann Benedict Listing 1847, Vorstudien zur Topologie
J.B. Listing (orbituary) Nature 27:316-317, 1883. “qualitative
geometry from the ordinary geometry in which quantitative relations
chiefly are treated.”
In the last 10 years move into the field of Point Clouds.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 14/31
26. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
→ An object representation contains enough information to reconstruct (an
approximation to) the object.
→ A description only contains enough information to identify an object as
amember of some class.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 15/31
27. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is Topology?
According to the Oxford English Dictionary,the word topology is derived from topos
(τ´oπoς) meaning place, and -logy (λoγ´ια), a variant of the verb λ´εγειυ, meaning to
speak. As such, topology speaks about places: how local neighborhoods connect to
each other to form a space.
Topology - Study of shapes (topological spaces) that can be deformed into
other shapes in a continuous manner (without tearing)
If one shape can be deformed into another, we say they are topologically
equivalent.
In contrast the field of euclidean geometry studies intrinsic properties in
the euclidean n-dimensional space that are invariant under euclidean
transformations, such as translations and ortogonal transformations in En
.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 16/31
28. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
What is Computational Topology?
Computational topology has theoretical and practical goals.
Theoretically, looks at the tractability and complexity of each problem, as well
as the design of efficient data structures and algorithms.
Practically, involves heuristics and fast software for solving problems that arise
in diverse disciplines.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 17/31
29. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Shape of data matters
End Introduction.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 18/31
30. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
1 About
2 Introduction
3 Elements of Topology
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 19/31
31. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Topology
A topology on a set X is a subset T ⊆ X such that:
a) ∅, X ∈ T
b) Uα ∈ T ⇒
α
Uα ∈ T
c) U1, . . . , Un ∈ T ⇒
n
k=1
Uk ∈ T
A subset U ∈ T is called open set and its complement X U is called closed.
The pair (X, T ) is called a topological space.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 20/31
32. DEPARTAMENTO DE INFORMATICA
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Topology / Topology
A function f : (X, Tx ) → (Y, Ty ) is continuous if for every open set
U ∈ (Y, Ty ), f −1
(U) is open in (X, Tx ), then f is called a continuous map.
U
(X,Tx)
f -1
(U)
(Y,Ty )
f
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 21/31
33. DEPARTAMENTO DE INFORMATICA
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Topology / Topology
In a topological space (X, Tx ), the closure U of some subset set U is the
intersection of all closed sets containing U
(X,Tx) (X,Tx )
UU
Some spanish synonyms are: adherencia, cierre.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 22/31
34. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Topology
The interior int(U) of some U ∈ (X, Tx ) is the union of all open sets contained
in U
(X,Tx)
U
(X,Tx )
int (U)
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 23/31
35. DEPARTAMENTO DE INFORMATICA
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Topology / Topology
The boundary ∂U of some U is ∂U = U − int(U)
(X,Tx)
U
(X,Tx )
U
(X,Tx )
int (U)
(X,Tx )
U∂
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 24/31
36. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Topology
A limit point A is a set of {x ∈ U : ∀ U open set in (X, Tx ),
x ∈ U ⇒ (U − x) U = ∅}
(X,Tx) (X,Tx )
A
Ux
A'
Spanish synonyms are: acumulaci´on.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 25/31
37. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Manifolds
(a) 1 (b) 2 (d) 2
(e) Klein bottle(c) Toruswith ∂
Figure: Manifolds. (a) The only compact connected one-manifold is a circle S1. (b) The sphere is a two-manifold. (c) The surface of
a donut, a torus, is also a two-manifold.(d) A Boy’s surface is a geometric immersion of the projective plane P2, a nonorientable
two-manifold. (e) The Klein bottle is another nonorientable two-manifold.
A topological space may be viewed as an abstraction of a metric space.
Similarly, manifolds generalize the connectivity of d − dimensional
Euclidean spaces Rd
by being locally similar but globally different.
A d − dimensional chart at p ∈ X is a homeomorphism ϕ : U → Rd
onto
an open subset of Rd
, where U is a neighborhood of p and open is
defined using a metric.
A d-dimensional manifold (d-manifold) is a topological space X with a
d − dimensional chart at every point x ∈ X.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 26/31
38. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
To compute information about a topological space using a computer, we need
a finite representation of the space.
In this section, we represent a topological space as a union of simple pieces,
deriving a combinatorial description that is useful in practice. Intuitively, cell
complexes are composed of Euclidean pieces glued together along seams,
generalizing polyhedra.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 27/31
39. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
Geometrical
(a) (b) (c) (d)
A set of (k + 1) points {u0, · · · , uk } in R (with k ≥ 0) is called affinely
independent if the k vectors (uj − u0) , with 1 ≤ j ≤ k are linearly
independent.
A k − simplex σ is defined as their convex hull spaned by {u0, · · · , uk }
points
k is called the dimension of σ and {u0, · · · , uk } the vertices of σ.
Simplices in dimension 0, 1, 2, 3 are called vertices (a), edge(b),
triangles(c), and tetrahedra(d),
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 28/31
40. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
Geometrical
The simplex is the set of points defined by all convex combinations
σ =
k
i=0
λi ui | λi > 0and
k
i=0
λi = 1
An l − simplex τ is called a face of a k − simplex σ if the vertices of τ are a
subset of the vertices of σ. Clearly, l ≤ k holds in this case. A k − simplex has
2k+1
− 1 faces.
A k-simplex has exactly 2k+1 − 1 faces. For instance, a tetrahedron consists
has one face in dimension 3 (itself), four triangular faces, six edges, and four
vertices which adds up to 15 = 24 − 1.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 29/31
41. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
Geometrical
Simplicial complexes.
A geometric simplicial complex K is a finite collection of simplices in Rd
such
that
for any simplex sigma in K, every face of σ is also in K, and.
for any two simplices σ, τ in K, the intersection σ ∩ τ is empty or a face
of both σ and τ (and therefore,part of K as well).
the dimension of K is the maximal dimension of the simplices that it contains.
Note that K is a set of subsets of Rd
and not a subset of Rd
.
K is defined as
|K| =
σ∈K
σ
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 30/31
42. DEPARTAMENTO DE INFORMATICA
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Topology / Simplicial complexes
Abstract
Given abstract vertices v1, · · · , vn , an abstract simplicial complex A is defined
as a collection of subsets of {v1, · · · , vn}, such that with any set M ∈ A, every
nonempty subset of M is also in A.
The subsets of cardinality k + 1 are called abstract k − simplices of A.
We can draw an abstract complex in Rd
by mapping each vertex to some point
in Rd
and mapping a abstract k − simplex to the simplex spanned by the k + 1
corresponding points. If this drawing is a geometric simplicial complex, we call
it a geometric realization of A.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 31/31
43. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
Abstract
Given abstract vertices v1, · · · , vn , an abstract simplicial complex A is defined
as a collection of subsets of {v1, · · · , vn}, such that with any set M ∈ A, every
nonempty subset of M is also in A.
The subsets of cardinality k + 1 are called abstract k − simplices of A.
We can draw an abstract complex in Rd
by mapping each vertex to some point
in Rd
and mapping a abstract k − simplex to the simplex spanned by the k + 1
corresponding points. If this drawing is a geometric simplicial complex, we call
it a geometric realization of A.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 31/31
44. DEPARTAMENTO DE INFORMATICA
INVESTIGACION Y POSTGRADO
Topology / Simplicial complexes
Abstract
Given abstract vertices v1, · · · , vn , an abstract simplicial complex A is defined
as a collection of subsets of {v1, · · · , vn}, such that with any set M ∈ A, every
nonempty subset of M is also in A.
The subsets of cardinality k + 1 are called abstract k − simplices of A.
We can draw an abstract complex in Rd
by mapping each vertex to some point
in Rd
and mapping a abstract k − simplex to the simplex spanned by the k + 1
corresponding points. If this drawing is a geometric simplicial complex, we call
it a geometric realization of A.
Rodrigo Rojas Moraleda — Introduction to Topological Data Analysis) (TDA) 31/31