This document provides an introduction to manifold learning. It defines what a manifold is and discusses how data lies on low-dimensional manifolds even when represented in high-dimensional space. It introduces several linear and nonlinear manifold learning algorithms, including Principal Components Analysis, Multidimensional Scaling, Isomap, Locally Linear Embedding, and Laplacian Eigenmaps. For each algorithm, it provides a brief overview of the motivation, key steps, and examples of applications like super-resolution imaging.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fifth part which is discussing singular value decomposition and principal component analysis.
Here are the slides of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
Here are the slides of the fourth part which is discussing eigenvalues and eigenvectors.
https://www.slideshare.net/CeniBabaogluPhDinMat/4-linear-algebra-for-machine-learning-eigenvalues-eigenvectors-and-diagonalization
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
A comprehensive tutorial on Convolutional Neural Networks (CNN) which talks about the motivation behind CNNs and Deep Learning in general, followed by a description of the various components involved in a typical CNN layer. It explains the theory involved with the different variants used in practice and also, gives a big picture of the whole network by putting everything together.
Next, there's a discussion of the various state-of-the-art frameworks being used to implement CNNs to tackle real-world classification and regression problems.
Finally, the implementation of the CNNs is demonstrated by implementing the paper 'Age ang Gender Classification Using Convolutional Neural Networks' by Hassner (2015).
Random Forest Algorithm - Random Forest Explained | Random Forest In Machine ...Simplilearn
This Random Forest Algorithm Presentation will explain how Random Forest algorithm works in Machine Learning. By the end of this video, you will be able to understand what is Machine Learning, what is classification problem, applications of Random Forest, why we need Random Forest, how it works with simple examples and how to implement Random Forest algorithm in Python.
Below are the topics covered in this Machine Learning Presentation:
1. What is Machine Learning?
2. Applications of Random Forest
3. What is Classification?
4. Why Random Forest?
5. Random Forest and Decision Tree
6. Comparing Random Forest and Regression
7. Use case - Iris Flower Analysis
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
5. Linear Algebra for Machine Learning: Singular Value Decomposition and Prin...Ceni Babaoglu, PhD
The seminar series will focus on the mathematical background needed for machine learning. The first set of the seminars will be on "Linear Algebra for Machine Learning". Here are the slides of the fifth part which is discussing singular value decomposition and principal component analysis.
Here are the slides of the first part which was discussing linear systems: https://www.slideshare.net/CeniBabaogluPhDinMat/linear-algebra-for-machine-learning-linear-systems/1
Here are the slides of the second part which was discussing basis and dimension:
https://www.slideshare.net/CeniBabaogluPhDinMat/2-linear-algebra-for-machine-learning-basis-and-dimension
Here are the slides of the third part which is discussing factorization and linear transformations.
https://www.slideshare.net/CeniBabaogluPhDinMat/3-linear-algebra-for-machine-learning-factorization-and-linear-transformations-130813437
Here are the slides of the fourth part which is discussing eigenvalues and eigenvectors.
https://www.slideshare.net/CeniBabaogluPhDinMat/4-linear-algebra-for-machine-learning-eigenvalues-eigenvectors-and-diagonalization
Machine Learning Foundations for Professional ManagersAlbert Y. C. Chen
20180526@Taiwan AI Academy, Professional Managers Class.
Covering important concepts of classical machine learning, in preparation for deep learning topics to follow. Topics include regression (linear, polynomial, gaussian and sigmoid basis functions), dimension reduction (PCA, LDA, ISOMAP), clustering (K-means, GMM, Mean-Shift, DBSCAN, Spectral Clustering), classification (Naive Bayes, Logistic Regression, SVM, kNN, Decision Tree, Classifier Ensembles, Bagging, Boosting, Adaboost) and Semi-Supervised learning techniques. Emphasis on sampling, probability, curse of dimensionality, decision theory and classifier generalizability.
ODSC India 2018: Topological space creation & Clustering at BigData scaleKuldeep Jiwani
Every data has an inherent natural geometry associated with it. We are generally influenced by how the world visually appears to us and apply the same flat Euclidean geometry to data. The data geometry could be curved, may have holes, distances cannot be defined in all cases. But if we still impose Euclidean geometry on it, then we may be distorting the data space and also destroying the information content inside it.
In the space of BigData world we have to regularly handle TBs of data and extract meaningful information from it. We have to apply many Unsupervised Machine Learning techniques to extract such information from the data. Two important steps in this process is building a topological space that captures the natural geometry of the data and then clustering in that topological space to obtain meaningful clusters.
This talk will walk through "Data Geometry" discovery techniques, first analytically and then via applied Machine learning methods. So that the listeners can take back, hands on techniques of discovering the real geometry of the data. The attendees will be presented with various BigData techniques along with showcasing Apache Spark code on how to build data geometry over massive data lakes.
International Journal of Engineering Inventions (IJEI) provides a multidisciplinary passage for researchers, managers, professionals, practitioners and students around the globe to publish high quality, peer-reviewed articles on all theoretical and empirical aspects of Engineering and Science.
Deformable Part Models are Convolutional Neural NetworksWei Yang
Girshick, Ross, et al. "Deformable part models are convolutional neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Mammalian Pineal Body Structure and Also Functions
Manifold learning
1. A Brief Introduction to Manifold Learning
Wei Yang
platero.yang@gmail.com
2016/8/11 1
Some slides are from Geometric Methods and Manifold Learning in Machine Learning (Mikhail Belkin and
Partha Niyoqi). Summer School (MLSS), Chicago 2009
2. What is a manifold?
2016/8/11 2
https://en.wikipedia.org/wiki/Manifold
3. Manifolds in visual perception
Consider a simple example of image variability, the set 𝑀 of all
facial images generated by varying the orientation of a face
• 1D manifold:
– a single degree of freedom: the angle of rotation
• The dimensionality of 𝑀 would increase if we allow
– image scaling
– illumination changing
– …
2016/8/11 3
The Manifold Ways of Perception
•H. Sebastian Seung and
•Daniel D. Lee
Science 22 December 2000
4. Why Manifolds?
• Euclidean distance in the high dimensional input space may
not accurately reflect the intrinsic similarity
– Euclidean distance
– Geodesic distance
2016/8/11 4
Linear Manifold VS. Nonlinear
Manifold
17. Principal Components Analysis
• Given x1, x2… , x 𝑛 ∈ ℝ 𝐷 with mean 0
• Find 𝑦1, 𝑦2 … , 𝑦𝑛 ∈ ℝ such that
𝑦𝑖 = w ∙ x 𝑖
• And
argmax
w
𝑣𝑎𝑟({𝑦𝑖}) =
𝑖
𝑦𝑖
2 = w 𝑇
𝑖
x𝑖x𝑖
𝑇 w
• w∗ is leading eigenvectors of 𝑖 x𝑖x𝑖
𝑇
2016/8/11 17
18. Multidimensional Scaling
• MDS: exploring similarities or dissimilarities in data.
• Given 𝑁 data points with distance function is defined as:
𝛿𝑖,𝑗
• The dissimilarity matrix can be defined as:
Δ ≔
𝛿1,1 … 𝛿1,𝑁
⋮ ⋱ ⋮
𝛿 𝑁,1 … 𝛿 𝑁,𝑁
Find x1, x2… , x 𝑁 ∈ ℝ 𝐷 such that
min
x1,... ,x 𝑁
𝑖<𝑗
( x 𝑖 − x𝑗 − 𝛿𝑖,𝑗)2
2016/8/11 18
21. Isomap: Motivation
• PCA/MDS see just the Euclidean structure
• Only geodesic distances reflect the true low-dimensional
geometry of the manifold
• The question:
– How to approximate geodesic distances?
2016/8/11 21
22. Isomap
1. Construct neighborhood graph 𝒅 𝒙(𝒊, 𝒋) using Euclidean
distance
– 𝜖-Isomap: neighbors within a radius 𝜖
– 𝐾-Isomap: 𝐾 nearest neighbors
2. Compute shortest path as the approximation of geodesic
distance
1. 𝒅 𝑮 𝒊, 𝒋 = 𝒅 𝒙(𝒊, 𝒋)
2. For 𝒌 = 𝟏, 𝟐, … , 𝑵, replace all 𝒅 𝑮 𝒊, 𝒋 by 𝐦𝐢𝐧 𝒅 𝑮 𝒊, 𝒋 , 𝒅 𝑮 𝒊, 𝒌 + 𝒅 𝑮 𝒌, 𝒋
3. Construct 𝑑-dimensional embedding using MDS
2016/8/11 22
25. Locally Linear Embedding
• Intuition: each data point and its neighbors are expected to lie
on or close to a locally linear patch of the manifold.
2016/8/11 25
26. Locally Linear Embedding
1. Assign neighbours to
each data point (𝑘-NN)
2. Reconstruct each
point by a weighted
linear combination of
its neighbors.
3. Map each point to
embedded
coordinates.
2016/8/11 26
S T Roweis, and L K Saul Science 2000;290:2323-2326
27. Steps of locally linear embedding
• Suppose we have 𝑁 data points 𝑋𝑖 in a 𝐷 dimensional space.
• Step 1: Construct neighborhood graph
– 𝑘-NN neighborhood
– Euclidean distance or normalized dot products
2016/8/11 27
28. Steps of locally linear embedding
• Step 2: Compute the weights 𝑊𝑖𝑗 that best linearly
reconstruct 𝑋𝑖 from its neighbors by minimizing
where
2016/8/11 28
29. Steps of locally linear embedding
• Step 3: Compute the low-dimensional embedding best
reconstructed by 𝑊𝑖𝑗 by minimizing
• Note: 𝑊 is a sparse matrix, and 𝑖-th row is barycentric
coordinates (center of mass) of 𝑋𝑖 in the basis of its nearest
neighbors.
• Similar to PCA, using lowest eigenvectors of 𝐼 − 𝑊 𝑇(𝐼 − 𝑊)
to embed.
2016/8/11 29
30. LLE (Comments by Ruimao Zhang)
算法优点
• LLE算法可以学习任意维的局部线性
的低维流形.
• LLE算法中的待定参数很少, K 和 d.
• LLE算法中每个点的近邻权值在平
移, 旋转,伸缩变换下是保持不变的.
• LLE算法有解析的整体最优解,不需
迭代.
• LLE算法归结为稀疏矩阵特征值计
算, 计算复杂度相对较小, 容易执行.
算法缺点
• LLE算法要求所学习的流形只能是不
闭合的且在局部是线性的.
• LLE算法要求样本在流形上是稠密采
样的.
• LLE算法中的参数 K, d 有过多的选择.
• LLE算法对样本中的噪音很敏感.
2016/8/11 30
31. Laplacian Eigenmaps
• Using the notion of the Laplacian of a graph to compute a
low-dimensional representation of the data
– The laplacian of a graph is analogous to the Laplace Beltrami operator
on manifolds, of which the eigenfunctions have properties desirable
for embedding (See M. Belkin and P. Niyogi for justification).
2016/8/11 31
32. Laplacian matrix (discrete Laplacian)
• Laplacian matrix is a matrix representation of a graph
𝐿 = 𝐷 − 𝐴
– 𝐿 is the Laplacian matrix
– 𝐷 is the degree matrix
– 𝐴 is the adjacent matrix
2016/8/11 32
36. Justification of optimal embedding
• We have constructed a weighted graph 𝐺 = 𝑉, 𝐸
• We want to map 𝐺 to a line 𝒚 so that connected points stay
as close together as possible
𝒚 = 𝑦1, 𝑦2, … , 𝑦𝑛
𝑻
• This can be done by minimizing the objective function
𝑖𝑗
𝑦𝑖 − 𝑦𝑗
2
𝑊𝑖𝑗
• It incurs a heavy penalty if neighboring points are mapped far
apart.
2016/8/11 36
38. Justification of optimal embedding (Cont.)
The minimization problem reduces to finding
Note the constraint removes an arbitrary scaling factor in the
embedding.
Using Lagrange multiplier and setting the derivative with respect
to 𝒚 equal to zero, we obtain
The optimum is given by the minimum eigenvalue solution to the
generalized eigenvalue problem (trivial solution: 𝒚 = 𝟏, 𝜆 = 0).
2016/8/11 38
41. Super-Resolution Through Neighbor Embedding
• Intuition: small patches in the low- and high-resolution images
form manifolds with similar local geometry in two distinct
spaces.
• X: low-resolution image Y: target high-resolution image
• The algorithm is extremely analogous to LLE!
– Step 1: construct neighborhood of each patch in X
– Step 2: compute the reconstructing weights of the neighbors that
minimize the reconstruction error
– Step 3: perform high-dimensional embedding to (as opposed to the
low-dimensional embedding of LLE)
– Step 4: Construct the target high-resolution image Y by enforcing local
compatibility and smoothness constraints between adjacent patches
obtained in step 3.
2016/8/11 41
Chang, Hong, Dit-Yan Yeung, and Yimin Xiong. "Super-resolution
through neighbor embedding." CVPR 2004.
42. Super-Resolution Through Neighbor Embedding
• Training parameters
– The number of nearest neighbors K
– The patch size
– The degree of overlap
2016/8/11 42
Chang, Hong, Dit-Yan Yeung, and Yimin Xiong. "Super-resolution
through neighbor embedding." CVPR 2004.
43. Super-Resolution Through Neighbor Embedding
2016/8/11 43
Chang, Hong, Dit-Yan Yeung, and Yimin Xiong. "Super-resolution
through neighbor embedding." CVPR 2004.
44. Laplacianfaces
• Mapping face images in the image space via Locality
Preserving Projections (LPP) to low-dimensional face
subspace (manifold), called Laplacianfaces.
• LLP is analogous to Laplacian Eigenmaps except the objective
function
– Laplacian Eigenmaps:
– LLP:
2016/8/11 44
He, Xiaofei, et al. "Face recognition using laplacianfaces." Pattern Analysis
and Machine Intelligence, IEEE Transactions on 27.3 (2005): 328-340.
45. Laplacianfaces
Learning Laplacianfaces for Representation
1. PCA projection (kept 98 percent information in the sense of
reconstruction error)
2. Constructing the nearest-neighbor graph
3. Choosing the weights
4. Optimize
The k lowest eigenvectors of
are choosing to form
is the so-called Laplacianfaces.
2016/8/11 45
image can be identified with a point in an abstract image space.
M is continuous because the image varies smoothly as the face is rotated.
It is a curve because it is generated by varying a single degree of freedom, the angle of rotation.
PCA can only learn linear manifolds.
PCA希望找到最能区分数据的投影方向.
project data in high dimentional space to a line (1D space). find a projection that maximize the variation of the data. Note in the PPT xi has mean zero.
we got some points from manifold. we use mesh or graph structure to approximate the structure of manifold (and this may be bad). connect nearby points together, and we can only measure EU distance in EU space. and if we want to do something on manifold, I will do them on this graph instead.
The intrinsic dimensionality of the data can be estimated by looking for the “elbow” at which this curve ceases to decrease significantly with added dimensions.
Open triangles: PCA, MDS(A-C)
Open circle: MDS
Solid circle: Isomap
A: face varying in pose & illumination
B: Swiss roll
C: hand: finger extension & wrist rotation
D: digit 2
The locally linear embedding algorithm of Roweis and Saul computes a different local quantity, the coefficients of the best approximation to a data point by a weighted linear combination of its neighbors. Then the algorithm finds a set of low-dimensional points, each of which can be linearly approximated by its neighbors with the same coefficients that were determined from the high-dimensional data points. Both algorithms yield impressive results on some benchmark artificial data sets, as well as on “real world” data sets. Importantly, they succeed in learning nonlinear manifolds, in contrast to algorithms such as PCA.
动机: 每个点及他的邻居可以认为处于同一块(或相近的)manifold上的线性patch上. 这样该点就可以由其邻居线性组合来表达.
The locally linear embedding algorithm of Roweis and Saul computes a different local quantity, the coefficients of the best approximation to a data point by a weighted linear combination of its neighbors. Then the algorithm finds a set of low-dimensional points, each of which can be linearly approximated by its neighbors with the same coefficients that were determined from the high-dimensional data points. Both algorithms yield impressive results on some benchmark artificial data sets, as well as on “real world” data sets. Importantly, they succeed in learning nonlinear manifolds, in contrast to algorithms such as principal co
We show that the embedding provided by the Laplacian eigenmap algorithm preserves local information optimally in a certain sense.
这里考虑一维的情况
We show that the embedding provided by the Laplacian eigenmap algorithm preserves local information optimally in a certain sense.
这里考虑一维的情况
We show that the embedding provided by the Laplacian eigenmap algorithm preserves local information optimally in a certain sense.
这里考虑一维的情况