SlideShare a Scribd company logo
1 of 1
Download to read offline
Abstract
The purpose of this research is to generate a
new type of matching algorithm in R^D.
Common and established approaches deal
with datasets that have equal pairwise dis-
tances of points, and can superimpose them
by a series of Euclidean motions. By calculat-
ing some value, there is then some metric for
how similar two datasets are. A problem arises
in matching two datasets that have unequal
pairwise distances. The focus of this study is re-
stricting the pairwise distances to be equal
within some reasonable value. We look into
creating an actual example of this form, by
moving from the isometric case to the
near-isometric case.
Objectives & Hypothesis:
The focus of the project was to bring to life a
working example of our method. While we
were confident that the techniques and ideas
projected in the paper of focus were correct,
we also needed to verify that they worked as
expected.
By expanding the idea of image mapping to
account for small variations, our techniques
can let us gain information about a data set
from its corresponding map.
Methods:
First and foremost, a strong focus on under-
standing the underlying mathematics was
necessary. After that, the purpose became to
the machinery working. Dubbed “slow twists”,
implementation of these matrices would
rotate datasets at a rate much slower than ex-
pected.
We started with the Procrustes problem. With
two point sets with equal distances between
all points, there exists set of Euclidean mo-
tions(translations, slides, and rotations) that
can superimpose the two.
Project Sponsor: Dr. Steven Damelin, Department of Mathematics
Student Researchers: Brad Schwartz, Sean Kelly
Conclusions:
In the future, we will work to create ideal synthetic
datasets on which to experiment, and get a final im-
plementation. We will continue to expand our focus
to non-Euclidean analogues. We plan to generalize
current image mapping algorithms, and expand our
work to high dimensions.
References:
S. B. Damelin and C. Fefferman, Extension, interpolation and matching in R^D, arxiv:1411.2451.
S. B. Damelin and C. Fefferman, Extensions in R^D, arxiv:1411.2468.
S. B. Damelin and C. Fefferman, On Extensions of e Diffeomorphisms, preprint, arxiv:1505.06950
Charles Fefferman, Steven. B. Damelin and William Glover, BMO Theorems for epsilon distorted diffeomor-
phisms on R^D and an application to comparing manifolds of speech and sound, Involve, a Journal of
Mathematics 5-2 (2012), 159--172. DOI 10.2140/involve.2012.5.159
Epsilon-Distorted Diffeomorphisms for
Interpolation and Matching in R^D, D ≥ 2
Illustrating slow twist as original dataset is rotated 90 degrees in R^2

More Related Content

Viewers also liked

Apresentação projeto integrado de aprendizagem
Apresentação projeto integrado de aprendizagemApresentação projeto integrado de aprendizagem
Apresentação projeto integrado de aprendizagemJBronze
 
Final bapp arts online shopping searching literature copy
Final bapp arts online shopping   searching literature copyFinal bapp arts online shopping   searching literature copy
Final bapp arts online shopping searching literature copyPaula Nottingham
 
Screenbeans , 100 diapositivas
Screenbeans , 100 diapositivas Screenbeans , 100 diapositivas
Screenbeans , 100 diapositivas martina aggio
 
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons!
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons! Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons!
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons! Canadian Patient Safety Institute
 
Prezentacja1. styczen audio 10 mb
Prezentacja1. styczen audio 10 mbPrezentacja1. styczen audio 10 mb
Prezentacja1. styczen audio 10 mbMariusz Majewski
 
Ejercicio 1 programacion algoritmos
Ejercicio 1 programacion algoritmosEjercicio 1 programacion algoritmos
Ejercicio 1 programacion algoritmosdinubazan
 
M3 online session 1 wbs3760 25.2.16
M3 online session 1 wbs3760 25.2.16 M3 online session 1 wbs3760 25.2.16
M3 online session 1 wbs3760 25.2.16 Paula Nottingham
 
Updated m3 session 2 26.10.16 to upload
Updated m3 session 2 26.10.16 to uploadUpdated m3 session 2 26.10.16 to upload
Updated m3 session 2 26.10.16 to uploadPaula Nottingham
 
Tumores intraoculares
Tumores intraocularesTumores intraoculares
Tumores intraocularesDiego Mascato
 
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...Learning from the best: A webinar with the Patient Safety Champion Awards Fin...
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...Canadian Patient Safety Institute
 
Module 1 5.10.16 Session 1
Module 1 5.10.16 Session 1 Module 1 5.10.16 Session 1
Module 1 5.10.16 Session 1 Paula Nottingham
 
Tips for patient family engagement with health authorities to improve patient...
Tips for patient family engagement with health authorities to improve patient...Tips for patient family engagement with health authorities to improve patient...
Tips for patient family engagement with health authorities to improve patient...Canadian Patient Safety Institute
 
Physician Education - Infection Prevention
Physician Education - Infection PreventionPhysician Education - Infection Prevention
Physician Education - Infection PreventionWeb Administrator
 
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...Zuora, Inc.
 

Viewers also liked (18)

Summer_Work
Summer_WorkSummer_Work
Summer_Work
 
Apresentação projeto integrado de aprendizagem
Apresentação projeto integrado de aprendizagemApresentação projeto integrado de aprendizagem
Apresentação projeto integrado de aprendizagem
 
Final bapp arts online shopping searching literature copy
Final bapp arts online shopping   searching literature copyFinal bapp arts online shopping   searching literature copy
Final bapp arts online shopping searching literature copy
 
Screenbeans , 100 diapositivas
Screenbeans , 100 diapositivas Screenbeans , 100 diapositivas
Screenbeans , 100 diapositivas
 
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons!
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons! Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons!
Ce n’est pas ce que nous faisons qui compte, c’est COMMENT nous le faisons!
 
Derechos de autor
Derechos de autor Derechos de autor
Derechos de autor
 
Prezentacja1. styczen audio 10 mb
Prezentacja1. styczen audio 10 mbPrezentacja1. styczen audio 10 mb
Prezentacja1. styczen audio 10 mb
 
Ejercicio 1 programacion algoritmos
Ejercicio 1 programacion algoritmosEjercicio 1 programacion algoritmos
Ejercicio 1 programacion algoritmos
 
M3 online session 1 wbs3760 25.2.16
M3 online session 1 wbs3760 25.2.16 M3 online session 1 wbs3760 25.2.16
M3 online session 1 wbs3760 25.2.16
 
Rick Smith Resume
Rick Smith ResumeRick Smith Resume
Rick Smith Resume
 
Updated m3 session 2 26.10.16 to upload
Updated m3 session 2 26.10.16 to uploadUpdated m3 session 2 26.10.16 to upload
Updated m3 session 2 26.10.16 to upload
 
Tumores intraoculares
Tumores intraocularesTumores intraoculares
Tumores intraoculares
 
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...Learning from the best: A webinar with the Patient Safety Champion Awards Fin...
Learning from the best: A webinar with the Patient Safety Champion Awards Fin...
 
Module 1 5.10.16 Session 1
Module 1 5.10.16 Session 1 Module 1 5.10.16 Session 1
Module 1 5.10.16 Session 1
 
Tips for patient family engagement with health authorities to improve patient...
Tips for patient family engagement with health authorities to improve patient...Tips for patient family engagement with health authorities to improve patient...
Tips for patient family engagement with health authorities to improve patient...
 
Physician Education - Infection Prevention
Physician Education - Infection PreventionPhysician Education - Infection Prevention
Physician Education - Infection Prevention
 
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...
Subscribed 2016: The Enterprise Shift - From a Perpetual to a Subscription Bu...
 
Ejercicios Metodología Futbol Base Atletico Madrid
Ejercicios Metodología Futbol Base Atletico MadridEjercicios Metodología Futbol Base Atletico Madrid
Ejercicios Metodología Futbol Base Atletico Madrid
 

Similar to UROP Symposium Poster

A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONmathsjournal
 
SVM - Functional Verification
SVM - Functional VerificationSVM - Functional Verification
SVM - Functional VerificationSai Kiran Kadam
 
IEEE Datamining 2016 Title and Abstract
IEEE  Datamining 2016 Title and AbstractIEEE  Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstracttsysglobalsolutions
 
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...Giovanni Sileno
 
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...Chris Rackauckas
 
Christopher Johnson Bachelor's Thesis
Christopher Johnson Bachelor's ThesisChristopher Johnson Bachelor's Thesis
Christopher Johnson Bachelor's ThesisBagpipesJohnson
 
Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02irisshicat
 
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCHK-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCHNexgen Technology
 
Soft Computing Techniques Based Image Classification using Support Vector Mac...
Soft Computing Techniques Based Image Classification using Support Vector Mac...Soft Computing Techniques Based Image Classification using Support Vector Mac...
Soft Computing Techniques Based Image Classification using Support Vector Mac...ijtsrd
 
Data enriched linear regression
Data enriched linear regressionData enriched linear regression
Data enriched linear regressionSunny Kr
 
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAEFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAcsandit
 
Asilomar09 compressive superres
Asilomar09 compressive superresAsilomar09 compressive superres
Asilomar09 compressive superresHoàng Sơn
 
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATIONSCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATIONNexgen Technology
 

Similar to UROP Symposium Poster (20)

A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
 
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATIONA NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
A NEW METHOD OF CENTRAL DIFFERENCE INTERPOLATION
 
ME Synopsis
ME SynopsisME Synopsis
ME Synopsis
 
SVM - Functional Verification
SVM - Functional VerificationSVM - Functional Verification
SVM - Functional Verification
 
IEEE Datamining 2016 Title and Abstract
IEEE  Datamining 2016 Title and AbstractIEEE  Datamining 2016 Title and Abstract
IEEE Datamining 2016 Title and Abstract
 
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...
Similarity and Contrast on Conceptual Spaces for Pertinent Description Genera...
 
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...
Generalizing Scientific Machine Learning and Differentiable Simulation Beyond...
 
Christopher Johnson Bachelor's Thesis
Christopher Johnson Bachelor's ThesisChristopher Johnson Bachelor's Thesis
Christopher Johnson Bachelor's Thesis
 
poster09
poster09poster09
poster09
 
Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02Shape matching and object recognition using shape context belongie pami02
Shape matching and object recognition using shape context belongie pami02
 
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCHK-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH
K-SUBSPACES QUANTIZATION FOR APPROXIMATE NEAREST NEIGHBOR SEARCH
 
Efficient projections
Efficient projectionsEfficient projections
Efficient projections
 
Efficient projections
Efficient projectionsEfficient projections
Efficient projections
 
Soft Computing Techniques Based Image Classification using Support Vector Mac...
Soft Computing Techniques Based Image Classification using Support Vector Mac...Soft Computing Techniques Based Image Classification using Support Vector Mac...
Soft Computing Techniques Based Image Classification using Support Vector Mac...
 
Data enriched linear regression
Data enriched linear regressionData enriched linear regression
Data enriched linear regression
 
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATAEFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA
EFFICIENTLY PROCESSING OF TOP-K TYPICALITY QUERY FOR STRUCTURED DATA
 
Asilomar09 compressive superres
Asilomar09 compressive superresAsilomar09 compressive superres
Asilomar09 compressive superres
 
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATIONSCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION
SCALABLE SEMI-SUPERVISED LEARNING BY EFFICIENT ANCHOR GRAPH REGULARIZATION
 

UROP Symposium Poster

  • 1. Abstract The purpose of this research is to generate a new type of matching algorithm in R^D. Common and established approaches deal with datasets that have equal pairwise dis- tances of points, and can superimpose them by a series of Euclidean motions. By calculat- ing some value, there is then some metric for how similar two datasets are. A problem arises in matching two datasets that have unequal pairwise distances. The focus of this study is re- stricting the pairwise distances to be equal within some reasonable value. We look into creating an actual example of this form, by moving from the isometric case to the near-isometric case. Objectives & Hypothesis: The focus of the project was to bring to life a working example of our method. While we were confident that the techniques and ideas projected in the paper of focus were correct, we also needed to verify that they worked as expected. By expanding the idea of image mapping to account for small variations, our techniques can let us gain information about a data set from its corresponding map. Methods: First and foremost, a strong focus on under- standing the underlying mathematics was necessary. After that, the purpose became to the machinery working. Dubbed “slow twists”, implementation of these matrices would rotate datasets at a rate much slower than ex- pected. We started with the Procrustes problem. With two point sets with equal distances between all points, there exists set of Euclidean mo- tions(translations, slides, and rotations) that can superimpose the two. Project Sponsor: Dr. Steven Damelin, Department of Mathematics Student Researchers: Brad Schwartz, Sean Kelly Conclusions: In the future, we will work to create ideal synthetic datasets on which to experiment, and get a final im- plementation. We will continue to expand our focus to non-Euclidean analogues. We plan to generalize current image mapping algorithms, and expand our work to high dimensions. References: S. B. Damelin and C. Fefferman, Extension, interpolation and matching in R^D, arxiv:1411.2451. S. B. Damelin and C. Fefferman, Extensions in R^D, arxiv:1411.2468. S. B. Damelin and C. Fefferman, On Extensions of e Diffeomorphisms, preprint, arxiv:1505.06950 Charles Fefferman, Steven. B. Damelin and William Glover, BMO Theorems for epsilon distorted diffeomor- phisms on R^D and an application to comparing manifolds of speech and sound, Involve, a Journal of Mathematics 5-2 (2012), 159--172. DOI 10.2140/involve.2012.5.159 Epsilon-Distorted Diffeomorphisms for Interpolation and Matching in R^D, D ≥ 2 Illustrating slow twist as original dataset is rotated 90 degrees in R^2