Manifold Learning
[News][Papers][Unorganized Papers][Presentations][Websites][Source Codes][Acknowledgments]
Tony Lin,Hongbin Zha, and Sang Uk Lee,Riemannian Manifold Learning for Nonlinear Dimensionality Reduction, in ECCV 2006, A. Leonardis, H. Bischof, and A. Prinz (Eds.): Part I, LNCS 3951, pp. 44-55, 2006. Springer-Verlag Berlin Heidelberg 2006.
Deli Zhao, Formulating LLE using alignment technique, Pattern Recognition 39 (2006), 2233-2235.
A. Brun , C.-F. Westin, M. Herberthson, H. Knutsson, Fast Manifold Learning Based on Riemannian Normal Coordinates, SCIA 2005, Joensuu, Finland, Proceedings of the 14th Scandinavian conference on image analysis (SCIA'05), June, 2005.
Philippos Mordohai, Gerard Medioni, Dimensionality Estimation and Manifold Learning in high-dimensional Spaces by Tensor Voting, IJCAI 05. [Tensot Voting]
J. Giesen and U. Wagner, Shape Dimension and Intrinsic Metric from Samples of Manifolds with High Co - dimension , Proceedings of the 19th Annual ACM Symposium on Computational Geometry (SoCG), pp. 329-337, 2003.
U. von Luxburg, M. Belkin, and O. Bousquet, Consistency of Spectral Clustering, Technical Report 134, Max Planck Institute for Biological Cybernetics, 2004.
[Foundations and Theories][Algorithms][Principal Curves and Generative Models][Dimensionaltiy Estimation][Applications][Others]
F.R. Bach and M.I. Jordan, Learning Graphical Models with Mercer Kernels, In NIPS 15, 2003.
Mikhail Belkin, Partha Niyogi, Semi-Supervised Learning on Riemannian Manifolds, To appear in Journal of Machine Learning Research.
Mikhail Belkin, Partha Niyogi, Semi-supervised learning on manifolds,Technical Report, 2002. Mikhail Belkin, Partha Niyogi, Laplacian Eigenmaps for dimensionality reduction and data representation,Technical Report, 2001. M. Belkin and P. Niyogi, Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering, In T. G. Dietterich, S. Becker, and Z. Ghahramani,editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press, 2002. M. Belkin and P. Niyogi, Using Manifold Structure for Partially Labelled Classification, Advances in NIPS, vol. 15, 2003.
Yoshua Bengio, Jean-Francois Paiement and Pascal Vincent, Out-of-Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering. M. Bernstein, V. de Silva, J.C. Langford, J. B. Tenenbaum, Graph approximation to geodesics on embedded manifolds, Technical Report, 2000. Matthew Brand, Subspace mappings for image sequences, Techincal Report, 2002. Matthew Brand, A unifying theorem for spectral embedding and clustering, Techincal Report, 2002. Matthew Brand, Continuous nonlinear dimensionality reduction by kernel eigenmaps. TR-2003-21 April 2003.
Odest Chadwicke Jenkins, Maja J Matari, A Spatio-temporal Extension to Isomap Nonlinear Dimension Reduction, ICML04. Denis Chigirev and William Bialek, Optimal Manifold Representation of Data:An Information Theoretic Approach, NIPS 2003. David L. Donoho and Carrie Grimes, Image manifolds which are isometric to Euclidean space. David L. Donoho and Carrie Grimes, Local ISOMAP Perfectly Recovers the underlying Parametrization of Occluded/Lacunary Libraries of Articulated Images, Technical Report 2002-27: Department of Statistics, Stanford University, 2002.
Joan Glaunes, Alain Trouve, Laurent Younes,Diffeomorphic matching of distributions: A new approach for unlabelled point-sets and sub-manifolds matching, CVPR04. Ulf Grenander,Toward a theory of natural scenes, 2003. Ulf Grenander, Pattern of thought, 2003. Ulf Grenander, clutter16: Computer understanding of Natural Scenes, 2002. Ulf Grenander, clutter17: Non-diffeomorphic background deformations, 2002. Ulf Grenander, Heuristic inference in sensory perception with an application to computer vision, 2002. Jihun Ham, Daniel D. Lee, Sebastian Mika, Bernhard Schkolkopf, A kernel view of the dimensionality reduction of manifolds, TR-110, Max Planck Institute for Biological Cybernetics. Husby, A Model for recognition of 3D Non-Dense Objects in Range Images, 2001.
Tony Jebara, Convex Invariance Learning, aistats2003. Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul , An Introduction to variational Methods for Graphical Models, Machine Learning, vol. 37, no. 2, pp.183-233, 1999. Martin H. C. Law, Nan Zhang, Anil K. Jain, Nonlinear Manifold Learning For Data Stream, In Proceedings of SIAM Data Mining, 2004.
Facundo Memoli, Guillermo Sapiro, Distance Functions and Geodesics on Points Clouds, Technical Report 1902, the Institute of Mathematics and its Appicatoins (IMA), 2002. Mumford, The statistical Description of Visual Signals, 2002. David Mumford, Pattern Theory: the Mathematics of Perception, ICM 2002,Vol, III · 1-3 Navarro, DJ & Lee, MD, Spatial Visualisation of Document Similarity, Proceedings of the Defence Human Factors Special Interest Group Meeting, August, 2001, 39-44. JinHyeong Park, Z. Zhang, Hongyuan Zha and R. Kasturi, Local Smoothing for Manifold Learning, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 04), vol.2, 452-459, 2004. Kim S. Pedersen and Ann B. Lee, Toward a full probability model of edges in natural images, APPTS TR #02-1, Division of Applied Mathematics, Brown University, January 2002. Robert Pless and Ian Simon, Embedding Images in non-Flat Spaces(Pless,2001), In Conference on Imaging Science Systems and Technology, pages 182-188, 2002.
H. S. Seung, How the brain keeps the eyes still, Proc. Natl. Acad. Sci. USA 93, 13339 (1996).
H. S. Seung, Learning Continuous Attractors in Recurrent Networks, Advances in Neural Information Processing Systems 10, 654-660 (1998).
H. S. Seung, and D.L. Daniel, The Manifold Ways of Perception, Science,12, pp.2268-2269, 2000. V. de Silva, J. B. Tenenbaum, Global Versus local methods in nonlinear dimensionality reduction, Advances in Neural Information Processing Systems 15. S. Becker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, pp. 705-712, 2002. http://web.mit.edu/cocosci/Papers/nips02-localglobal-in-press.pdf Vin de Silva, and Joshua B. Tenenbaum, Unsupervised Learning of curved manifolds, in Proceedings of the MSRI workshop on nonlinear estimation and classification. Springer Verlag, 2002. J. B. Tenenbaum, Learning the structure of similarity, NIPS96. J. B. Tenenbaum, Mapping a manifold of perceptual observations, NIPS97. Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul, Learning a Kernel Matrix for Nonlinear Dimensionality Reduction, ICML04. J. Zhang, L. He, and Z. H. Zhou, "Analyzing Magnification Factors and Principal Spead Directions in Manifold Learning", in Proceedings of the 9th Online World Conference on Soft Computing in Industrial Applications (WSC9), 2004. H. Zha and Z. Zhang, ISOMETRIC EMBEDDING AND CONTINUUM ISOMAP, ICML, pp.864-871, 2003. ZhenYue Zhang, HongYuan Zha, Principal Manifolds And Nonlinear Dimension Reduction Via Local Tangent Space Alignment.
ZhenYue Zhang, HongYuan Zha, Local Linear Smoothing For Nonlinear Manifold Learning.
Xiaojin Zhu, Zoubin Ghahramani, John Lafferty, Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions, ICML03.
Balasubramanian, The Isomap Algorithm and Topological Stability and response , Science, vol. 295, 2002.
Bourlard, Auto-Association by Multilayer Perceptrons and Singular Value Decomposition, Biological Cybernetics, vol. 59, pp. 291-294, 1988. Matthew Brand, MERL, Charting a manifold, Neural Information Proceeding Systems: Natural and Synthetic}, Vancouver, Canada, December 9-14, 2002. David Cohn, Informed Projections, In NIPS14, 2002. Ofer Dekel, Yoram Singer, Multiclass Learning by Probabilistic Embeddings, In NIPS 14, 2002. David DeMers, and Garrison Cottrell, Non-linear Dimensionality Reduction, Advances in Neural Information Processing Systems 5, pp. 580 - 587, 1992. Xin Geng, De-Chuan Zhan, and Zhi-Hua Zhou, Supervised Nonlinear Dimensionality Reduction for Visualization and Classification, IEEE Trans. on SMCB, 2004. Jose Gomes and Aleksandra Mojsilovic, A variational approach to recovering a manifold from sample points, 7th European Conference on Computer Vision(ECCV) 2002, May 2002, Copenhagen, Denmark.
Xiaofei He, Partha Niyogi, Locality Preserving Projections, Advances in Neural Information Processing Systems 16 (NIPS 2003), Vancouver, Canada, 2003.
Hérault J, Guérin-Dugué A, Villemain P., Searching for the embedded manifolds in high-dimensional data, problems and unsolved questions, ESANN 2002.
Hinton, Roweis, Stochastic Neighbor Embedding, NIPS02.
G.E. Hinton, Z. Ghahramani, Generative Models for Discovering Sparse Distribution Representations, Philos Trans R Soc Lond B Biol Sci. 1997 Aug 29;352(1358):1177-90.
G.E. Hinton, P. Dayan, and M. Revow., Modelling the Manifolds of Images of HandWritten Digits, IEEE Transactions on Neural Networks, 8(1): 65-74, 1997.
G. E. Hinton, M. Revow, and P. Dayan, Recognizing Handwritten Digits Using Mixtures of Linear Models, Advances in Neural Information Processing System, 7, 1015- 1022, 1995.
Xiaoming Huo, Jihong Chen, LOCAL LINEAR PROJECTION (LLP),
Sepp Hochreiter , Klaus Obermayer, Feature Selection and Classification on Matrix Data: From Large Margins To Small Covering Numbers, In NIPS 14, 2002.
Rong Jin, Zoubin Ghahramani, Learning with Multiple labels, In NIPS 14 ,2002.
J. A. Lee, A. Lendasse, M. Verleysen, Curvilinear Distance Analysis versus ISOMAP, Accepted for publications in Neurocomputing, 2001.
Dörthe Malzahn , Manfred Opper, A Variational Approach to learning Curves, NIPS 2001 : 463-469.
Marzia Polito, Grouping and Dimensionality Reduction by Locally Linear Embedding.
Dick de Ridder, and Vojtech Franc, Robust manifold learning, Technical report CTU-CMP-2003-08, Center for Machine Perception, Department of Cybernetics Faculty of Electrical Engineering, Czech Technical University, Prague, 2003, pp. 1-36.
Dick de Ridder, Olga Kouropteva, Oleg Okun, Matti Pietikainen and Robert P.W. Duin, Supervised locally linear embedding, ICANN03.
Dick de Ridder, Marco Loog and Marcel J.T. Reinders, Local Fisher embedding, ICPR04.
Dick de Ridder and Robert P.W. Duin, Locally linear embedding for classification, Number PH-2002-01 in the Pattern Recognition Group Technical Report Series, (submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence).
Roman Rosipal, Leonard J.Trejo, Andrzej Cichocki, Kernel Principla component regression with EM Approach to Nonlinear Principal Components Extraction, Technical Report, University of Paisley, 2001.
V. Roth and J. Laub and J. Buhmann and K. Muller, Going Metric:Denoising pairwise data, In NIPS14, 2002.
Lawrence Saul and Sam Roweis, An Introduction to Locally Linear Embedding , [Draft Version, 2000] .
S.T. Roweis, EM algorithms for PCA and SPCA, In M.I. Jordan, M.J. Kearns, and S.A. Solla, editors, Advances in Neural Information Processing Systems 10 . MIT Press, Cambridge, MA, 1997. (2)
S. T. Roweis, and K. S. Lawrance, Nonlinear Dimensionality reduction by locally linear embedding, Science, 290, pp. 2323-2326, 2000.
S. T. Roweis, Lawrence S. and G. Hinton, Global Coordination of local linear models(Roweis), in Neural Information Processing Systems, volume 14, 2002.
Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia, Spikernels: Embedding Spiking Neurons in Inner-Product Spaces, Advances in Neural Information Processing Systems (NIPS) 15 MIT Press, Cambridge, MA, 2003
Yee Whye Teh and Sam Roweis, Automatic Alignment of Local Representations, In NIPS14, 2002.
J. B. Tenenbaum, de Silva, V. and Langford, J.C, A global geometric framework for nonlinear dimensionality reduction, Science, 290, pp. 2319-2323, 2000.
M. Tipping and C. Bishop, Probabilistic principal component analysis, Technical Report NCRG/97/010, Neural Computing Research Group, Aston University, Birmingham, UK, September 1997.
J.J. Verbeek and N. Vlassis and B. Krose, Coordinating Principal Component Analyzers, in Proceedings of International Conference on Artificial Neural Networks, Madrid, Spain, 2002.
J.J. Verbeeky, Sam T. Roweis, and Nikos Vlassisy, Non-linear CCA and PCA by Alignment of Local Models, NIPS 2003. Sethu Vijayakumar and Stefan Schaal, Local Dimensionality Reduction For Locally Weighted Learning, Proc. IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA'97), July 10-11,1997,Monterey, California, pp.220-225 (1997). J. J. Verbeek, N. Vlassis, B. Krose, Fast nonlinear dimensionality reduction with topology preserving networks ,ESANN 2002. Y. Weiss, Segmentation using eigenvectors: a unifying view, in Proceedings IEEE International Conference on Computer Vision p. 975-982 (1999) Vincent, P. and Bengio, Y., Manifold Parzen Windows, In NIPS14, 2002. Jason Weston, Olivier Chapelle, Andre Elisseeff, Bernhard Schoelkopf and Vladimir Vapnik, Kernel Dependency Estimation,(98), Max Planck Institute for Biological Cybernetics, Tübingen, Germany, August, 2002.(Code and Examples)
J. Zhang, H. Shen, and Z. H. Zhou, "Unified Locally Linear Embedding and Linear Discriminant Analysis Algorithm (ULLELDA) for Face Recognition", Advances in Biometric Personal Authentication. Stan Z. Li, Jianhuang Lai, Tieniu Tan, Guocan Feng, Yunhong Wang (Ed.), LNCS 3338, Springer-Verlag, 2004, pp. 209-307, 2004.
J. Zhang, S. Z. Li, "Adaptive Nonlinear Auto-Associative Modeling through Manifold Learning", T. B. Ho, D. Cheung, and H. Liu (Eds.): PAKDD 2005, LNAI 3518, pp. 599-604, Springer-Verlag, Berlin Heidelberg, 2005.
Principal Curves and
Generative Models 
Cristian Sminchisescu, Allan Jepson, Generative Modeling for Continuous Non-Linearly Embedded Visual Inference, ICML04. Principal Curves Sources
Jose A. Costa and Alfred O. Hero III, MANIFOLD LEARNING USING EUCLIDEAN K-NEAREST NEIGHBOR GRAPHS, icassp04.
Jose A. Costa, Geodesic Entropic Graphs for Dimension and Entropy Estimation in Manifold Learning, to appear in IEEE Trans. on Signal Processing, August, 2004. Elizaveta Levina, Peter J. Bickel, Maximum Likelihood Estimation of Intrinsic Dimension.
Karl W. Pettis, Thomas A. Bailey, Anil K. Jain, and Richard C. Dubes, An Intrinsic Dimensionality Estimator From Near-Neighbor Information. IEEE Trans. on PAMI, Vol, PAMI-1, No. 1, 1979, pp: 25-37. Kegl, Intinsic Dimension Estimation Using Packing Numbers, NIPS 2002.
Ognjen Arandjelovic, Roberto Cipolla, Face Recognition from Face Motion Manifolds using Robust Kernel Resistor-Average Distance.FPIV2004.
C. Bregler and S. Omohundro, Surface Learning with Applications to Lipreading, NIPS (Advances in Neural Information Processing Systems 6, Morgan Kaufmann Publishers), 1994 C. Bregler, S.Omohundro, Nonlinear Image Interpolation using Manifold Learning, in Advances in Neural Information Processing Systems 7, 1995 Anders Brun, Hae-Jeong Park, Hans Knutsson, and Carl-Fredrik Westin, Coloring of DT-MRI Fiber Traces using Laplacian Eigenmaps, Proceedings of the Ninth International Conference on Computer Aided Systems Theory (EUROCAST), Vol. 2809, February, 2003. Hong Chang, Dit-Yan Yeung, Yimin Xiong, Super-Resolution Through Neighbor Embedding, CVPR04.
Ya Chang, Changbo Hu, Matthew Turk, Probabilistic Expression Analysis on Manifolds, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'04), Vol. 2, pp. 520-527, 2004. D. Cheng, J. Zhang, S. Tang, and J. Wang, "Freeway Traffic Stream Modelling based on Principal Curves and its Analysis", IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp.246-258, 2004. Michael Revow, Christopher K. I. Williams and Geoffrey E. Hinton, Using mixtures of deformable models to capture variations in hand printed digits, In: Third International workshop on Frontiers in Handwriting Recognition, Buffalo, USA. pp 142-152. Dennis Decoste, Visualizing Mercer Kernel Feature Spaces Via Kernelized Locally-Linear Embeddings, The 8th International. Conference on Neural Information Processing (ICONIP-2001), November 2001.
Ahmed Elgammal and Chan-Su Lee, Inferring 3D Body Pose from Silhouettes using Activity Manifold Learning, CVPR04.
Ahmed Elgammal and Chan-Su Lee, Separating Style and Content on a Nonlinear Manifold, CVPR04. Changbo Hu, Ya Chang, Rogerio Feris, and Matthew Turk, Manifold Based Analysis of Facial Expression, 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Vol. 5, June 27 - July 02, p. 81, 2004.
Olga Kouropteva, Oleg Okun, Matti Pietikainen, Classification of handwritten digits using supervised locally linear embedding algorithm and support vector machine, ESANN03. Kulpinski, LLE and ISOMAP Analysis of Spectra and Colour Images, Master thesis, Simon Fraser University,2002. Leslie, Christina, Eleazar Eskin, Adiel Cohen, Jason Weston and William Stafford Noble,Mismatch String Kernels for SVM Protein Classification, Neural Information Processing Systems 15, 2003. pp. 1441-1448. Amnon Shashua, Anat Levin, Shai Avidan, Manifold Pursuit: A New Approach to Appearance Based Recognition, ICPR02. Miguel L. Teodoro, George N. Phillips Jr, and Lydia E. Kavraki, A Dimensionality Reduction Approach to Modeling Protein Flexibility. In Proceedings of the Sixth Annual Conference on Computational Molecular Biology RECOMB 2002.
Michail Vlachos, Carlotta Domeniconi, Dimitrios Gunopulos, Non-Linear Dimensionality Reduction Techniques for Classification and Visualization, KDD02. J. P. Vert, Graph-driven features extraction from microarray data using diffusion kernels and kernel CCA, In NIPS 14, 2002.
J. Zhang, S. Z. Li, and J. Wang, "Nearest Manifold Approach for Face Recognition", The 6th IEEE International Conference on Automatic Face and Gesture Recogntion, Seoul, Korea, May, 2004.
J. Zhang, S. Z. Li, and J. Wang, "Manifold Learning and Applications in Recognition". in Intelligent Multimedia Processing with Soft Computing. Yap Peng Tan, Kim Hui Yap, Lipo Wang (Ed.), Springer-Verlag, Heidelberg, 2004. Alice Zheng, Deconstruction Motion, 2000.
Others
Odest Chadwicke Jenkins and Maja J Matari,Deriving Action and Behavior Primitives from Human Motion Data, IROS 2002.
Chavez and E. Navarro and G. Baeza-Yates and R. Marroqu, Searching in Metric Spaces, Technical Report TR/DCC-99-3, Dept. of Computer Science, Univ. of Chile (1999) To appear in ACM Computing Surveys, 1999.
Pattern Matching and Lossy Data Compression on Random Fields(Kontoyiannis, 2001)
G. Tzanetakis and P. Cook, Automatic Musical Genre Classification of Audio Signals, IEEE Trans. Speech Audio Processing, vol. 10, no. 5, pp. 293--302, Jul. 2002.
Eric P. Xing, Andrew Y. Ng, Michael I. Jordan and Stuart Russell, Distance Metric Learning with application to clustering with side-information, NIPS 02.
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Schkolkopf, Learning with Local and Global Consistency.
Dengyong Zhou, Jason Weston, Arthur Gretton, Olivier Bousquet, and Bernhard Schkolkopf, Ranking on Data Manifolds.
Dengyong Zhou and Bernhard Schkolkopf, Learning from Labeled and Unlabeled Data Using Random Walks.
Dengyong Zhou, Bernhard Schkolkopf, A Regularization Framework for Learning from Graph Data.
Dengyong Zhou, Bernhard Schkolkopfy, and Thomas Hofmann, Semi-supervised Learning on Directed Graphs.