Image Similarities for Learning Video Manifolds. Selen Atasoy MICCAI 2011 Tutorial
|
|
|
- Rose Gibson
- 5 years ago
- Views:
Transcription
1 Image Similarities for Learning Video Manifolds Selen Atasoy MICCAI 2011 Tutorial
2 Image Spaces
![] [Roweis2000: S. T. Roweis, L. K.](/docs-images/94/121797870/images/3-3.jpg "Saul: Nonlinear dimensionality reduction by locally")
3 Image Manifolds Tenenbaum2000 Roweis2000 Tenenbaum2000 [Tenenbaum2000: J. B. Tenenbaum, V. Silva, J. C. Langford: A global geometric framework for nonlinear dimensionality reduction. Science, 290(5500), 2000.] [Roweis2000: S. T. Roweis, L. K. Saul: Nonlinear dimensionality reduction by locally linear embedding. Science, 290(5500), 2000]
![] [Atasoy2010: S. Atasoy, D.](/docs-images/94/121797870/images/4-4.jpg "Mateus, J. Lallemand, A.")
4 Video Manifolds Pless2003 Atasoy2010 [Pless2003: R. Pless: Using Isomap to Explore Video Sequences: ICCV, 2003.] [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.] [Atasoy2011: S. Atasoy, D. Mateus, A. Meining, G.Z. Yang, N. Navab: Targeted Optical Biopsies for Surveillance Endoscopies, MICCAI, 2011.]
5 Theoretical Background
6 High dimensional data points lying on or near a manifold Manifold Learning Low dimensional representation Find a mapping that best preserves???...
7 1. Define a matrix based on the relations between data points Manifold Learning A General Recipe 2. Compute the eigenvectors & eigenvalues 3. Embed each sample
8 Manifold Learning A General Recipe Method Operator/Matrix Preserved Objective Function PCA Covariance matrix Variance of the dataset / Euclidean distances between data points Laplacian Eigenmaps Graph Laplacian Distances within the local neighbourhood of each data point ISOMAP Geodesic distance matrix Geodesic distances between data points LLE Reconstruction weights Reconstruction weights within the local neighbourhood of each data point
9 Rayleigh-Ritz Theorem: Manifold Learning Why does it work? eigenvalues eigenvectors Recall: Scalar product: Scalar product in H: Norm: Norm in H:
10 Manifold Learning Why does it work? Discrete Domain vectors Continuous Domain functions Schwarz s Kernel Theorem: Each linear operator is given as an integration against a unique kernel. That kernel is the impulse response of the linear system to an impulse (a delta function).
11 Manifold Learning Why does it work? Discrete Domain vectors Continuous Domain functions
12 Manifold Learning Why does it work? Discrete Domain vectors Continuous Domain functions The matrix H defines: which operator is applied which (Hilbert) space we are working in which quantity will be conserved
13 Laplacian Eigenmaps
14 Solve Manifold Learning Laplacian Eigenmaps Find the eigenvectors of the graph Laplacian Equivalent to solving the Helmholtz Equation [Belkin2003: M. Belkin, P. Niyogi: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6), MIT Press, 2003]
![[Levy2010]](/docs-images/94/121797870/images/15-5.jpg "[Chladni1787: E.")
![] [Levy2010: B.](/docs-images/94/121797870/images/15-8.jpg "Levy: Spectral")
15 Manifold Learning Laplacian Eigenmaps - Interpretation [Chladni1787] [Levy2010] [Levy2010] [Chladni1787: E. Chladni: Discoveries in the Theory of Sound, 1787.] [Levy2010: B. Levy: Spectral Geometry Processing: ACM SIGGRAPH Course Notes, 2010.]
16 Non-linear Manifold Learning Laplacian Eigenmaps - Interpretation Manifold learning as bending, stretching without cutting or creating wholes Vibrational modes are preserved while bending the manifold
17 Endoscopic Video Manifolds (EVMs)
18 Endoscopic Video Manifolds Challenges Clustering Uninformative Frames
19 Endoscopic Video Manifolds Clustering Uninformative Frames
20 Endoscopic Video Manifolds Clustering Uninformative Frames Informative frame & power spectrum Uninformative frame & power spectrum Informative frame Uninformative frame [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.]
21 Endoscopic Video Manifolds Clustering Uninformative Frames [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.]
22 Significant change in endoscope viewpoint Endoscopic Video Manifolds Challenges Small overlap between frames showing the same scene Scenes do not necessarily contain distinctive features
23 Endoscopic Video Manifolds Clustering Endoscopic Scenes Euclidean Distance Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster frames 150 frames 137 frames 102 frames 98 frames 78 frames 71 frames 71 frames 44 frames 38 frames [Belkin2003: M. Belkin, P. Niyogi: Laplacian eigenmaps for dimensionality reduction and data representation. Neural computation, 15(6), MIT Press, 2003] [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.]
24 Endoscopic Video Manifolds Clustering Endoscopic Scenes Euclidean Distances Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster
25 Endoscopic Video Manifolds Clustering Endoscopic Scenes - NCC Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster frames 137 frames 103 frames 98 frames 85 frames 82 frames 81 frames 64 frames 44 frames 44 frames [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.]
26 Endoscopic Video Manifolds Clustering Endoscopic Scenes - NCC Euclidean Distance Normalized Cross Correlation [Atasoy2010: S. Atasoy, D. Mateus, J. Lallemand, A. Meining, G.Z. Yang, N. Navab: Endoscopic Video Manifolds, MICCAI, 2010.]
27 Endoscopic Video Manifolds Clustering Endoscopic Scenes - NCC
28 Endoscopic Video Manifolds Clustering Endoscopic Scenes with Temporal Constraints Change the adjacency matrix to include temporal constraints [Atasoy2011: S. Atasoy, D. Mateus, A. Meining, G.Z. Yang, N. Navab: Targeted Optical Biopsies for Surveillance Endoscopies, MICCAI, 2011]
29 Endoscopic Video Manifolds Clustering Endoscopic Scenes with Temporal Constraints Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 Cluster 6 Cluster 7 Cluster 8 Cluster 9 Cluster frames 143 frames 126 frames 120 frames 112 frames 88 frames 55 frames 53 frames 43 frames 43 frames [Atasoy2011: S. Atasoy, D. Mateus, A. Meining, G.Z. Yang, N. Navab: Targeted Optical Biopsies for Surveillance Endoscopies, MICCAI, 2011]
30
31 Acknowledgements Prof. Nassir Navab Prof. Guang-Zhong Yang Prof. Alexander Meining Dr. Diana Mateus Thank you for your attention!!!
Non-linear dimension reduction
Sta306b May 23, 2011 Dimension Reduction: 1 Non-linear dimension reduction ISOMAP: Tenenbaum, de Silva & Langford (2000) Local linear embedding: Roweis & Saul (2000) Local MDS: Chen (2006) all three methods
Robust Pose Estimation using the SwissRanger SR-3000 Camera
Robust Pose Estimation using the SwissRanger SR- Camera Sigurjón Árni Guðmundsson, Rasmus Larsen and Bjarne K. Ersbøll Technical University of Denmark, Informatics and Mathematical Modelling. Building,
Locality Preserving Projections (LPP) Abstract
Locality Preserving Projections (LPP) Xiaofei He Partha Niyogi Computer Science Department Computer Science Department The University of Chicago The University of Chicago Chicago, IL 60615 Chicago, IL
RDRToolbox A package for nonlinear dimension reduction with Isomap and LLE.
RDRToolbox A package for nonlinear dimension reduction with Isomap and LLE. Christoph Bartenhagen October 30, 2017 Contents 1 Introduction 1 1.1 Loading the package......................................
Data fusion and multi-cue data matching using diffusion maps
Data fusion and multi-cue data matching using diffusion maps Stéphane Lafon Collaborators: Raphy Coifman, Andreas Glaser, Yosi Keller, Steven Zucker (Yale University) Part of this work was supported by
Locality Preserving Projections (LPP) Abstract
Locality Preserving Projections (LPP) Xiaofei He Partha Niyogi Computer Science Department Computer Science Department The University of Chicago The University of Chicago Chicago, IL 60615 Chicago, IL
Large-Scale Face Manifold Learning
Large-Scale Face Manifold Learning Sanjiv Kumar Google Research New York, NY * Joint work with A. Talwalkar, H. Rowley and M. Mohri 1 Face Manifold Learning 50 x 50 pixel faces R 2500 50 x 50 pixel random
The Analysis of Parameters t and k of LPP on Several Famous Face Databases
The Analysis of Parameters t and k of LPP on Several Famous Face Databases Sujing Wang, Na Zhang, Mingfang Sun, and Chunguang Zhou College of Computer Science and Technology, Jilin University, Changchun
Technical Report. Title: Manifold learning and Random Projections for multi-view object recognition
Technical Report Title: Manifold learning and Random Projections for multi-view object recognition Authors: Grigorios Tsagkatakis 1 and Andreas Savakis 2 1 Center for Imaging Science, Rochester Institute
Manifold Clustering. Abstract. 1. Introduction
Manifold Clustering Richard Souvenir and Robert Pless Washington University in St. Louis Department of Computer Science and Engineering Campus Box 1045, One Brookings Drive, St. Louis, MO 63130 {rms2,
Dimension Reduction CS534
Dimension Reduction CS534 Why dimension reduction? High dimensionality large number of features E.g., documents represented by thousands of words, millions of bigrams Images represented by thousands of
SELECTION OF THE OPTIMAL PARAMETER VALUE FOR THE LOCALLY LINEAR EMBEDDING ALGORITHM. Olga Kouropteva, Oleg Okun and Matti Pietikäinen
SELECTION OF THE OPTIMAL PARAMETER VALUE FOR THE LOCALLY LINEAR EMBEDDING ALGORITHM Olga Kouropteva, Oleg Okun and Matti Pietikäinen Machine Vision Group, Infotech Oulu and Department of Electrical and
Isometric Mapping Hashing
Isometric Mapping Hashing Yanzhen Liu, Xiao Bai, Haichuan Yang, Zhou Jun, and Zhihong Zhang Springer-Verlag, Computer Science Editorial, Tiergartenstr. 7, 692 Heidelberg, Germany {alfred.hofmann,ursula.barth,ingrid.haas,frank.holzwarth,
Globally and Locally Consistent Unsupervised Projection
Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence Globally and Locally Consistent Unsupervised Projection Hua Wang, Feiping Nie, Heng Huang Department of Electrical Engineering
Global versus local methods in nonlinear dimensionality reduction
Global versus local methods in nonlinear dimensionality reduction Vin de Silva Department of Mathematics, Stanford University, Stanford. CA 94305 silva@math.stanford.edu Joshua B. Tenenbaum Department
Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding (CSSLE)
2016 International Conference on Artificial Intelligence and Computer Science (AICS 2016) ISBN: 978-1-60595-411-0 Remote Sensing Data Classification Using Combined Spectral and Spatial Local Linear Embedding
CIE L*a*b* color model
CIE L*a*b* color model To further strengthen the correlation between the color model and human perception, we apply the following non-linear transformation: with where (X n,y n,z n ) are the tristimulus
Hyperspectral image segmentation using spatial-spectral graphs
Hyperspectral image segmentation using spatial-spectral graphs David B. Gillis* and Jeffrey H. Bowles Naval Research Laboratory, Remote Sensing Division, Washington, DC 20375 ABSTRACT Spectral graph theory
Selecting Models from Videos for Appearance-Based Face Recognition
Selecting Models from Videos for Appearance-Based Face Recognition Abdenour Hadid and Matti Pietikäinen Machine Vision Group Infotech Oulu and Department of Electrical and Information Engineering P.O.
Differential Structure in non-linear Image Embedding Functions
Differential Structure in non-linear Image Embedding Functions Robert Pless Department of Computer Science, Washington University in St. Louis pless@cse.wustl.edu Abstract Many natural image sets are samples
Locally Linear Landmarks for large-scale manifold learning
Locally Linear Landmarks for large-scale manifold learning Max Vladymyrov and Miguel Á. Carreira-Perpiñán Electrical Engineering and Computer Science University of California, Merced http://eecs.ucmerced.edu
Manifold Learning for Video-to-Video Face Recognition
Manifold Learning for Video-to-Video Face Recognition Abstract. We look in this work at the problem of video-based face recognition in which both training and test sets are video sequences, and propose
Sparse Manifold Clustering and Embedding
Sparse Manifold Clustering and Embedding Ehsan Elhamifar Center for Imaging Science Johns Hopkins University ehsan@cis.jhu.edu René Vidal Center for Imaging Science Johns Hopkins University rvidal@cis.jhu.edu
Learning a Manifold as an Atlas Supplementary Material
Learning a Manifold as an Atlas Supplementary Material Nikolaos Pitelis Chris Russell School of EECS, Queen Mary, University of London [nikolaos.pitelis,chrisr,lourdes]@eecs.qmul.ac.uk Lourdes Agapito
Non-linear CCA and PCA by Alignment of Local Models
Non-linear CCA and PCA by Alignment of Local Models Jakob J. Verbeek, Sam T. Roweis, and Nikos Vlassis Informatics Institute, University of Amsterdam Department of Computer Science,University of Toronto
Sensitivity to parameter and data variations in dimensionality reduction techniques
Sensitivity to parameter and data variations in dimensionality reduction techniques Francisco J. García-Fernández 1,2,MichelVerleysen 2, John A. Lee 3 and Ignacio Díaz 1 1- Univ. of Oviedo - Department
Global versus local methods in nonlinear dimensionality reduction
Global versus local methods in nonlinear dimensionality reduction Vin de Silva Department of Mathematics, Stanford University, Stanford. CA 94305 silva@math.stanford.edu Joshua B. Tenenbaum Department
Non-Local Manifold Tangent Learning
Non-Local Manifold Tangent Learning Yoshua Bengio and Martin Monperrus Dept. IRO, Université de Montréal P.O. Box 1, Downtown Branch, Montreal, H3C 3J7, Qc, Canada {bengioy,monperrm}@iro.umontreal.ca Abstract
DIMENSION REDUCTION FOR HYPERSPECTRAL DATA USING RANDOMIZED PCA AND LAPLACIAN EIGENMAPS
DIMENSION REDUCTION FOR HYPERSPECTRAL DATA USING RANDOMIZED PCA AND LAPLACIAN EIGENMAPS YIRAN LI APPLIED MATHEMATICS, STATISTICS AND SCIENTIFIC COMPUTING ADVISOR: DR. WOJTEK CZAJA, DR. JOHN BENEDETTO DEPARTMENT
Generalized Principal Component Analysis CVPR 2007
Generalized Principal Component Analysis Tutorial @ CVPR 2007 Yi Ma ECE Department University of Illinois Urbana Champaign René Vidal Center for Imaging Science Institute for Computational Medicine Johns
Réduction de modèles pour des problèmes d optimisation et d identification en calcul de structures
Réduction de modèles pour des problèmes d optimisation et d identification en calcul de structures Piotr Breitkopf Rajan Filomeno Coelho, Huanhuan Gao, Anna Madra, Liang Meng, Guénhaël Le Quilliec, Balaji
COMPRESSED DETECTION VIA MANIFOLD LEARNING. Hyun Jeong Cho, Kuang-Hung Liu, Jae Young Park. { zzon, khliu, jaeypark
COMPRESSED DETECTION VIA MANIFOLD LEARNING Hyun Jeong Cho, Kuang-Hung Liu, Jae Young Park Email : { zzon, khliu, jaeypark } @umich.edu 1. INTRODUCTION In many imaging applications such as Computed Tomography
Lecture Topic Projects
Lecture Topic Projects 1 Intro, schedule, and logistics 2 Applications of visual analytics, basic tasks, data types 3 Introduction to D3, basic vis techniques for non-spatial data Project #1 out 4 Data
Assessment of Dimensionality Reduction Based on Communication Channel Model; Application to Immersive Information Visualization
Assessment of Dimensionality Reduction Based on Communication Channel Model; Application to Immersive Information Visualization Mohammadreza Babaee, Mihai Datcu and Gerhard Rigoll Institute for Human-Machine
School of Computer and Communication, Lanzhou University of Technology, Gansu, Lanzhou,730050,P.R. China
Send Orders for Reprints to reprints@benthamscienceae The Open Automation and Control Systems Journal, 2015, 7, 253-258 253 Open Access An Adaptive Neighborhood Choosing of the Local Sensitive Discriminant
Head Frontal-View Identification Using Extended LLE
Head Frontal-View Identification Using Extended LLE Chao Wang Center for Spoken Language Understanding, Oregon Health and Science University Abstract Automatic head frontal-view identification is challenging
Stratified Structure of Laplacian Eigenmaps Embedding
Stratified Structure of Laplacian Eigenmaps Embedding Abstract We construct a locality preserving weight matrix for Laplacian eigenmaps algorithm used in dimension reduction. Our point cloud data is sampled
Advanced Machine Learning Practical 2: Manifold Learning + Clustering (Spectral Clustering and Kernel K-Means)
Advanced Machine Learning Practical : Manifold Learning + Clustering (Spectral Clustering and Kernel K-Means) Professor: Aude Billard Assistants: Nadia Figueroa, Ilaria Lauzana and Brice Platerrier E-mails:
Iterative Non-linear Dimensionality Reduction by Manifold Sculpting
Iterative Non-linear Dimensionality Reduction by Manifold Sculpting Mike Gashler, Dan Ventura, and Tony Martinez Brigham Young University Provo, UT 84604 Abstract Many algorithms have been recently developed
Video sequences association for people re-identification across multiple non-overlapping cameras
Video sequences association for people re-identification across multiple non-overlapping cameras D-N. Truong Cong 1, C. Achard 2, L. Khoudour 1, L. Douadi 1 1 French National Institute for Transport and
Keywords: Linear dimensionality reduction, Locally Linear Embedding.
Orthogonal Neighborhood Preserving Projections E. Kokiopoulou and Y. Saad Computer Science and Engineering Department University of Minnesota Minneapolis, MN 4. Email: {kokiopou,saad}@cs.umn.edu Abstract
Appearance Manifold of Facial Expression
Appearance Manifold of Facial Expression Caifeng Shan, Shaogang Gong and Peter W. McOwan Department of Computer Science Queen Mary, University of London, London E1 4NS, UK {cfshan, sgg, pmco}@dcs.qmul.ac.uk
Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction
Alternative Model for Extracting Multidimensional Data Based-on Comparative Dimension Reduction Rahmat Widia Sembiring 1, Jasni Mohamad Zain 2, Abdullah Embong 3 1,2 Faculty of Computer System and Software
Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound
Manifold Learning for Image-Based Breathing Gating with Application to 4D Ultrasound Christian Wachinger, Mehmet Yigitsoy, Nassir Navab Computer Aided Medical Procedures (CAMP), TUM, Munich, Germany {wachinge,
Learning Appearance Manifolds from Video
Learning Appearance Manifolds from Video Ali Rahimi MIT CS and AI Lab, Cambridge, MA 9 ali@mit.edu Ben Recht MIT Media Lab, Cambridge, MA 9 brecht@media.mit.edu Trevor Darrell MIT CS and AI Lab, Cambridge,
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis Yiran Li yl534@math.umd.edu Advisor: Wojtek Czaja wojtek@math.umd.edu 10/17/2014 Abstract
Towards Multi-scale Heat Kernel Signatures for Point Cloud Models of Engineering Artifacts
Towards Multi-scale Heat Kernel Signatures for Point Cloud Models of Engineering Artifacts Reed M. Williams and Horea T. Ilieş Department of Mechanical Engineering University of Connecticut Storrs, CT
Dimension Reduction of Image Manifolds
Dimension Reduction of Image Manifolds Arian Maleki Department of Electrical Engineering Stanford University Stanford, CA, 9435, USA E-mail: arianm@stanford.edu I. INTRODUCTION Dimension reduction of datasets
Dynamic Facial Expression Recognition Using A Bayesian Temporal Manifold Model
Dynamic Facial Expression Recognition Using A Bayesian Temporal Manifold Model Caifeng Shan, Shaogang Gong, and Peter W. McOwan Department of Computer Science Queen Mary University of London Mile End Road,
A Stochastic Optimization Approach for Unsupervised Kernel Regression
A Stochastic Optimization Approach for Unsupervised Kernel Regression Oliver Kramer Institute of Structural Mechanics Bauhaus-University Weimar oliver.kramer@uni-weimar.de Fabian Gieseke Institute of Structural
Modelling and Visualization of High Dimensional Data. Sample Examination Paper
Duration not specified UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE Modelling and Visualization of High Dimensional Data Sample Examination Paper Examination date not specified Time: Examination
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis:midyear Report
Dimension reduction for hyperspectral imaging using laplacian eigenmaps and randomized principal component analysis:midyear Report Yiran Li yl534@math.umd.edu Advisor: Wojtek Czaja wojtek@math.umd.edu
Topology-Invariant Similarity and Diffusion Geometry
1 Topology-Invariant Similarity and Diffusion Geometry Lecture 7 Alexander & Michael Bronstein tosca.cs.technion.ac.il/book Numerical geometry of non-rigid shapes Stanford University, Winter 2009 Intrinsic
Texture Representations Using Subspace Embeddings
Texture Representations Using Subspace Embeddings Xiaodong Yang and YingLi Tian Department of Electrical Engineering The City College of New York, CUNY {xyang02, ytian}@ccny.cuny.edu Abstract In this paper,
Multidimensional scaling Based in part on slides from textbook, slides of Susan Holmes. October 10, Statistics 202: Data Mining
Multidimensional scaling Based in part on slides from textbook, slides of Susan Holmes October 10, 2012 1 / 1 Multidimensional scaling A visual tool Recall the PCA scores were X V = U where X = HX S 1/2
Reconstruction of Images Distorted by Water Waves
Reconstruction of Images Distorted by Water Waves Arturo Donate and Eraldo Ribeiro Computer Vision Group Outline of the talk Introduction Analysis Background Method Experiments Conclusions Future Work
Spectral Clustering X I AO ZE N G + E L HA M TA BA S SI CS E CL A S S P R ESENTATION MA RCH 1 6,
Spectral Clustering XIAO ZENG + ELHAM TABASSI CSE 902 CLASS PRESENTATION MARCH 16, 2017 1 Presentation based on 1. Von Luxburg, Ulrike. "A tutorial on spectral clustering." Statistics and computing 17.4
A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning
A Supervised Non-linear Dimensionality Reduction Approach for Manifold Learning B. Raducanu 1 and F. Dornaika 2,3 1 Computer Vision Center, Barcelona, SPAIN 2 Department of Computer Science and Artificial
Application of Spectral Clustering Algorithm
1/27 Application of Spectral Clustering Algorithm Danielle Middlebrooks dmiddle1@math.umd.edu Advisor: Kasso Okoudjou kasso@umd.edu Department of Mathematics University of Maryland- College Park Advance
A Taxonomy of Semi-Supervised Learning Algorithms
A Taxonomy of Semi-Supervised Learning Algorithms Olivier Chapelle Max Planck Institute for Biological Cybernetics December 2005 Outline 1 Introduction 2 Generative models 3 Low density separation 4 Graph
Semi-supervised Learning by Sparse Representation
Semi-supervised Learning by Sparse Representation Shuicheng Yan Huan Wang Abstract In this paper, we present a novel semi-supervised learning framework based on l 1 graph. The l 1 graph is motivated by
A Discriminative Non-Linear Manifold Learning Technique for Face Recognition
A Discriminative Non-Linear Manifold Learning Technique for Face Recognition Bogdan Raducanu 1 and Fadi Dornaika 2,3 1 Computer Vision Center, 08193 Bellaterra, Barcelona, Spain bogdan@cvc.uab.es 2 IKERBASQUE,
Classification Performance related to Intrinsic Dimensionality in Mammographic Image Analysis
Classification Performance related to Intrinsic Dimensionality in Mammographic Image Analysis Harry Strange a and Reyer Zwiggelaar a a Department of Computer Science, Aberystwyth University, SY23 3DB,
LEARNING ON STATISTICAL MANIFOLDS FOR CLUSTERING AND VISUALIZATION
LEARNING ON STATISTICAL MANIFOLDS FOR CLUSTERING AND VISUALIZATION Kevin M. Carter 1, Raviv Raich, and Alfred O. Hero III 1 1 Department of EECS, University of Michigan, Ann Arbor, MI 4819 School of EECS,
Clustering and Dimensionality Reduction
Clustering and Dimensionality Reduction Some material on these is slides borrowed from Andrew Moore's excellent machine learning tutorials located at: Data Mining Automatically extracting meaning from
Learning Manifolds in Forensic Data
Learning Manifolds in Forensic Data Frédéric Ratle 1, Anne-Laure Terrettaz-Zufferey 2, Mikhail Kanevski 1, Pierre Esseiva 2, and Olivier Ribaux 2 1 Institut de Géomatique et d Analyse du Risque, Faculté
Data Mining: Data. Lecture Notes for Chapter 2. Introduction to Data Mining
Data Mining: Data Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar Data Preprocessing Aggregation Sampling Dimensionality Reduction Feature subset selection Feature creation
Recognizing Handwritten Digits Using the LLE Algorithm with Back Propagation
Recognizing Handwritten Digits Using the LLE Algorithm with Back Propagation Lori Cillo, Attebury Honors Program Dr. Rajan Alex, Mentor West Texas A&M University Canyon, Texas 1 ABSTRACT. This work is
Discovering Shared Structure in Manifold Learning
Discovering Shared Structure in Manifold Learning Yoshua Bengio and Martin Monperrus Dept. IRO, Université de Montréal P.O. Box 1, Downtown Branch, Montreal, H3C 3J7, Qc, Canada {bengioy,monperrm}@iro.umontreal.ca
Robust Locally Linear Embedding
Robust Locally Linear Embedding Hong Chang Dit-Yan Yeung Department of Computer Science Hong Kong University of Science and Technology Clear Water Bay, Kowloon, Hong Kong Corresponding author: Dit-Yan
Non-Local Estimation of Manifold Structure
Non-Local Estimation of Manifold Structure Yoshua Bengio, Martin Monperrus and Hugo Larochelle Département d Informatique et Recherche Opérationnelle Centre de Recherches Mathématiques Université de Montréal
Alternative Statistical Methods for Bone Atlas Modelling
Alternative Statistical Methods for Bone Atlas Modelling Sharmishtaa Seshamani, Gouthami Chintalapani, Russell Taylor Department of Computer Science, Johns Hopkins University, Baltimore, MD Traditional
Proximal Manifold Learning via Descriptive Neighbourhood Selection
Applied Mathematical Sciences, Vol. 8, 2014, no. 71, 3513-3517 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.42111 Proximal Manifold Learning via Descriptive Neighbourhood Selection
Research Article Chaotic Neural Network for Biometric Pattern Recognition
Artificial Intelligence Volume 12, Article ID 124176, 9 pages doi:.1155/12/124176 Research Article Chaotic Neural Network for Biometric Pattern Recognition Kushan Ahmadian and Marina Gavrilova Department
A Geometric Perspective on Machine Learning
A Geometric Perspective on Machine Learning Partha Niyogi The University of Chicago Collaborators: M. Belkin, V. Sindhwani, X. He, S. Smale, S. Weinberger A Geometric Perspectiveon Machine Learning p.1
Non-Local Estimation of Manifold Structure
Neural Computation Manuscript #3171 Non-Local Estimation of Manifold Structure Yoshua Bengio, Martin Monperrus and Hugo Larochelle Département d Informatique et Recherche Opérationnelle Centre de Recherches
A Course in Machine Learning
A Course in Machine Learning Hal Daumé III 13 UNSUPERVISED LEARNING If you have access to labeled training data, you know what to do. This is the supervised setting, in which you have a teacher telling
Local Discriminant Embedding and Its Variants
Local Discriminant Embedding and Its Variants Hwann-Tzong Chen Huang-Wei Chang Tyng-Luh Liu Institute of Information Science, Academia Sinica Nankang, Taipei 115, Taiwan {pras, hwchang, liutyng}@iis.sinica.edu.tw
Representation Discovery in Planning using Harmonic Analysis
Representation Discovery in Planning using Harmonic Analysis Jeff Johns and Sarah Osentoski and Sridhar Mahadevan Computer Science Department University of Massachusetts Amherst Amherst, Massachusetts
Evgeny Maksakov Advantages and disadvantages: Advantages and disadvantages: Advantages and disadvantages: Advantages and disadvantages:
Today Problems with visualizing high dimensional data Problem Overview Direct Visualization Approaches High dimensionality Visual cluttering Clarity of representation Visualization is time consuming Dimensional
Learning Multiple Tasks using Manifold Regularization
Learning Multiple Tasks using Manifold Regularization Arvind Agarwal Hal Daumé III Department of Computer Science University of Maryland College Park, MD 0740 arvinda@cs.umd.edu hal@umiacs.umd.edu Samuel
Dimensionality reduction and visualisation of geoscientific images via locally linear embedding 1
Dimensionality reduction and visualisation of geoscientific images via locally linear embedding 1 Fabio Boschetti Fabio.Boschetti@csiro.au CSIRO Exploration & Mining PO Box 1130, Bentley WA 6102, Australia
Face Recognition using Tensor Analysis. Prahlad R. Enuganti
Face Recognition using Tensor Analysis Prahlad R. Enuganti The University of Texas at Austin Final Report EE381K 14 Multidimensional Digital Signal Processing May 16, 2005 Submitted to Prof. Brian Evans
Image Processing. Image Features
Image Processing Image Features Preliminaries 2 What are Image Features? Anything. What they are used for? Some statements about image fragments (patches) recognition Search for similar patches matching
Supervised Isomap with Dissimilarity Measures in Embedding Learning
Supervised Isomap with Dissimilarity Measures in Embedding Learning Bernardete Ribeiro 1, Armando Vieira 2, and João Carvalho das Neves 3 Informatics Engineering Department, University of Coimbra, Portugal
Learning Semantic Features for Action Recognition via Diffusion Maps
Learning Semantic Features for Action Recognition via Diffusion Maps Jingen Liu a, Yang Yang, Imran Saleemi and Mubarak Shah b a Department of EECS, University of Michigan, Ann Arbor, MI, USA b Department
Linear and Non-linear Dimentionality Reduction Applied to Gene Expression Data of Cancer Tissue Samples
Linear and Non-linear Dimentionality Reduction Applied to Gene Expression Data of Cancer Tissue Samples Franck Olivier Ndjakou Njeunje Applied Mathematics, Statistics, and Scientific Computation University
A Framework for Automated Measurement of the Intensity of Non-Posed Facial Action Units
A Framework for Automated Measurement of the Intensity of Non-Posed Facial Action Units Mohammad H. Mahoor 1, Steven Cadavid 2, Daniel S. Messinger 3, and Jeffrey F. Cohn 4 1 Department of Electrical and
Locality Condensation: A New Dimensionality Reduction Method for Image Retrieval
Locality Condensation: A New Dimensionality Reduction Method for Image Retrieval Zi Huang Heng Tao Shen Jie Shao Stefan Rüger Xiaofang Zhou School of Information Technology and Electrical Engineering,
Population Modeling in Neuroscience Using Computer Vision and Machine Learning to learn from Brain Images. Overview. Image Registration
Population Modeling in Neuroscience Using Computer Vision and Machine Learning to learn from Brain Images Overview 1. Part 1: Theory 1. 2. Learning 2. Part 2: Applications ernst.schwartz@meduniwien.ac.at
Face Recognition Using Wavelet Based Kernel Locally Discriminating Projection
Face Recognition Using Wavelet Based Kernel Locally Discriminating Projection Venkatrama Phani Kumar S 1, KVK Kishore 2 and K Hemantha Kumar 3 Abstract Locality Preserving Projection(LPP) aims to preserve
Overview. Spectral Processing of Point- Sampled Geometry. Introduction. Introduction. Fourier Transform. Fourier Transform
Overview Spectral Processing of Point- Sampled Geometry Introduction Fourier transform Spectral processing pipeline Spectral filtering Adaptive subsampling Summary Point-Based Computer Graphics Markus
Using Graph Model for Face Analysis
Report No. UIUCDCS-R-05-2636 UILU-ENG-05-1826 Using Graph Model for Face Analysis by Deng Cai, Xiaofei He, and Jiawei Han September 05 Using Graph Model for Face Analysis Deng Cai Xiaofei He Jiawei Han
Generating Different Realistic Humanoid Motion
Generating Different Realistic Humanoid Motion Zhenbo Li,2,3, Yu Deng,2,3, and Hua Li,2,3 Key Lab. of Computer System and Architecture, Institute of Computing Technology, Chinese Academy of Sciences, Beijing
A kernel-based active learning strategy for content-based image retrieval
A kernel-based active learning strategy for content-based image retrieval I. Daoudi, K. Idrissi Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR505, F-696, France {dimane, kidrissi@liris.cnrs.fr Abstract
Finding Structure in CyTOF Data
Finding Structure in CyTOF Data Or, how to visualize low dimensional embedded manifolds. Panagiotis Achlioptas panos@cs.stanford.edu General Terms Algorithms, Experimentation, Measurement Keywords Manifold
Visualizing breast cancer data with t-sne
Bachelor Thesis Visualizing breast cancer data with t-sne Student: Ka ka Tam s4074157 k.tam@student.ru.nl Supervisors: Dr. Ida Sprinkhuizen-Kuyper Dr. Elena Marchiori October 25, 2013 Contents 1 Introduction
Angular Decomposition
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence Angular Decomposition Dengdi Sun, Chris Ding 2,,BinLuo and Jin Tang School of Computer Science and Technology,
Graph based machine learning with applications to media analytics
Graph based machine learning with applications to media analytics Lei Ding, PhD 9-1-2011 with collaborators at Outline Graph based machine learning Basic structures Algorithms Examples Applications in
Enhanced Multilevel Manifold Learning
Journal of Machine Learning Research x (21x) xx-xx Submitted x/xx; Published xx/xx Enhanced Multilevel Manifold Learning Haw-ren Fang Yousef Saad Department of Computer Science and Engineering University