Nonlinear dimensionality reduction using a temporal coherence principle

Information Sciences

Volumn 181, Issue 16, 2011, Pages 3284-3307

  Huang, Yaping ^a   Zhao, Jiali ^a   Liu, Yunhui ^a   Luo, Siwei ^a   Zou, Qi ^a   Tian, Mei ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

Author keywords

Character recognition; Dimensionality reduction; Feature extraction; Manifold learning; Pattern classification; Temporal coherence principle; Temporal slowness principle

Indexed keywords

DIMENSIONALITY REDUCTION; MANIFOLD LEARNING; PATTERN CLASSIFICATION; TEMPORAL COHERENCE PRINCIPLE; TEMPORAL SLOWNESS PRINCIPLE;

CHARACTER RECOGNITION; CLUSTERING ALGORITHMS; DATA REDUCTION; EIGENVALUES AND EIGENFUNCTIONS; FEATURE EXTRACTION; INPUT OUTPUT PROGRAMS; NONLINEAR ANALYSIS; TIME SERIES; TIME SERIES ANALYSIS;

DATA MINING;

EID: 79957481207     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2011.04.001     Document Type: Article

References (52)

1. O. Arandjelovic, G. Shakhnarovich, J. Fisher, R. Cipolla, T. Darrell, Face recognition with image sets using manifold density divergence, in: IEEE Conference on Computer Vision and Pattern Recognition, vol. 15, 2005, pp. 581-588.
2. M. Balasubramanian, and E.L. Schwartz The isomap algorithm and topological stability Science 295 5552 2002 9
3. M. Belkin, and P. Niyogi Laplacian Eigenmaps and spectral techniques for embedding and clustering Advances in Neural Information Processing Systems 14 2001 585 591
4. M. Belkin, and P. Niyogi Laplacian Eigenmaps for dimensionality reduction and data representation Neural Computation 15 6 2003 1373 1396 (Pubitemid 37049796)
5. M. Belkin, P. Niyogi, and V. Sindhwani Manifold regularization: a geometric framework for learning from labeled and unlabeled examples Journal of Machine Learning Research 6 2006 2399 2424
6. P. Berkes, Temporal Slowness as an Unsupervised Learning Principle: Self-organization of Complex-cell Receptive Fields and Application to Pattern Recognition, Ph.D. Thesis, Institute for Theoretical Biology, Humboldt University, Berlin, 2005.
7. P. Berkes Pattern recognition with slow feature analysis Cognitive Sciences EPrint Archive (CogPrint) 4104 2005
8. D. Chelidze, and W. Zhou Smooth orthogonal decomposition based modal analysis Journal of Sound and Vibration 292 3-5 2006 461 473
9. D. Chelidze, and M. Liu Reconstructing slow-time dynamics from fasttime measurements Philosophical Transaction of the Royal Society A 366 2008 729 7445
10. D. Chelidze, and J.P. Cusumano Phase space warping: nonlinear time series analysis for slowly drifting systems Philosophical Transactions of the Royal Society A 364 2006 2495 2513 (Pubitemid 44328182)
11. R.R. Coifman, S. Lafon, A.B. Lee, M. Maggioni, B. Nadler, F. Warner, and S.W. Zucker Geometric diffusions as a tool for harmonic analysis and structure definition of data. Part I: Diffusion maps Proceedings of the National Academy of Sciences 102 21 2005 7426 7431 (Pubitemid 40740988)
12. P. Demartines, and J. Hérault Curvilinear component analysis: a selforganizing neural network for nonlinear mapping of data sets IEEE Transactions on Neural Networks 8 1 1997 148 154 (Pubitemid 127767788)
13. D.L. Donoho, C. Grimes, Hessian Eigenmaps: New Locally Linear Embedding Techniques for High-dimensional Data, Technical Report TR-2003-08, Department of Statistics, Stanford University, 2003.
14. W. Einhäuser, J. Hipp, J. Eggert, E. Körner, and P. König Learning viewpoint invariant object representation using a temporal coherence principle Biological Cybernetics 93 2005 79 90 (Pubitemid 41067713)
15. P. Földiák Learning invariance from transformation sequences Neural Computation 3 1991 194 200
16. X. Geng, D.C. Zhan, and Z.H. Zhou Supervised nonlinear dimensionality reduction for visualization and classification IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics 35 6 2005 1098 1107 (Pubitemid 41800411)
17. A. Guréin-Dugué, P. Teissier, G. Delso-Gafaro, J. Hérault, Curvilinear component analysis for high-dimensional data representation: II. Examples of introducing additional mapping constraints for specific applications, in: International Work-Conference on Artificial and Natural Neural Networks, Alicante, Spain, 1999.
18. X.F. He, and P. Niyogi Locality preserving projections Advances in Neural Information Processing Systems 16 2003
19. G. Hinton Connectionist learning procedures Artificial Intelligence 40 1989 185 234 (Pubitemid 20607828)
20. J. Hurri, and A. Hyvärinen Simple-cell-like receptive fields maximize temporal coherence in natural video Neural Computation 15 3 2003 663 691 (Pubitemid 37049821)
21. A. Hyvärinen, J. Hurri, and J. Väyrynen Bubbles: a unifying framework for low-level statistical properties of natural image sequences Optical Society of America 20 7 2003 1237 1252
22. C. Kayser, W. Einhäuser, O. Dümmer, P. König, K. Körding, Extracting slow subspaces from natural videos leads to complex cells, in: International Conference on Artificial Neural Networks, Vienna, Austria, 2001, pp. 1075-1080. (Pubitemid 33317082)
23. D. Keysers, T. Deselaers, C. Gollan, and H. Ney Deformation models for image recognition IEEE Transaction on Pattern Analysis and Machine Intelligence 29 8 2007 1422 1435 (Pubitemid 47040440)
24. T. Kohonen Self-organized formation of topologically correct feature maps Biological Cybernetics 43 1982 59 69 (Pubitemid 12139984)
25. K. Körding, C. Kayser, W. Einhäuser, and P. König How are complex cell properties adapted to the statistics of natural scenes? Journal of Neurophysiology 91 1 2004 206 212 (Pubitemid 38084365)
26. F. Lauer, C.Y. Suen, and G. Bloch A trainable feature extractor for handwritten digit recognition Pattern Recognition 40 6 2007 1816 1824 (Pubitemid 46402919)
27. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner Gradient-based learning applied to document recognition Proceedings of the IEEE 86 11 1998 2278 2324
28. J.B. Li, J.S. Pan, and S.C. Chu Kernel class-wise locality preserving projection Information Sciences 178 7 2008 1825 1835
29. R. De Maesschalck, D. Jouan-Rimbaud, and D.L. Massart The mahalanobis distance Chemometrics and Intelligent Laboratory Systems 50 1, 4 2000 1 18 (Pubitemid 30019315)
30. J.L. McFarland, and A.F. Fuchs Discharge patterns in nucleus prepositus hypoglossi and adjacent medial vestibular nucleus during horizontal eye movement in behaving macaques Journal of Neurophysiology 68 7 1992 319 332
31. G. Mitchison Removing time variation with the anti-Hebbian differential synapse Neural Computation 3 1991 312 320
32. A. Mojsilovic, B.E. Rogowitz, Capturing image semantics with low-level descriptors, in: International Conference on Image Processing, I, Thessaloniki, Greece, 2001, pp. 18-21. (Pubitemid 33057356)
33. B. Nadler, S. Lafon, R.R. Coifman, and I.G. Kevrekidis Diffusion maps, spectral clustering and eigenfunctions of Fokker-Planck operators Advances in Neural Information Processing Systems 18 2005
34. M. Ranzato, C.S. Poultney, S. Chopra, and Y. LeCun Efficient learning of sparse representations with an energy-based model Advances in Neural Information Processing Systems 19 2006
35. S.T. Roweis, and L.K. Saul Nonlinear dimensionality analysis by locally linear embedding Science 290 12 2000 2323 2326 (Pubitemid 32041578)
36. S.A. Sarcia, G. Cantone, V.R. Basili, Adopting curvilinear component analysis to improve software cost estimation accuracy: model, application strategy, and an experimental verification, in: Evaluation and Assessment in Software Engineering, University of Bari, Italy, 2008.
37. B. Schölkopf, A.J. Smola, and K.-R. Müller Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 5 1998 1299 1319 (Pubitemid 128463674)
38. H.S. Seung, and D.D. Lee The manifold ways of perception Science 290 12 2000 2268 2269 (Pubitemid 32041555)
39. P.Y. Simard, D. Steinkraus, J.C. Platt, Best practices for convolutional neural networks applied to visual document analysis, in: International Conference on Document Analysis and Recognition, vol. 2, 2003, pp. 958-962.
40. Y.Q. Song, F.P. Nie, C.S. Zhang, and S.M. Xiang A unified framework for semi-supervised dimensionality reduction Pattern Recognition 41 9 2008 2789 2799
41. J.V. Stone, and A.J. Bray A learning rule for extracting spatio-temporal invariances Network: Computation in Neural Systems 6 3 1995 429 436
42. J.S. Taube Head direction cells and the neurophysiological basis for a sense of direction Progress in Neurobiology 55 3 1998 225 256 (Pubitemid 28240005)
43. P. Teissier, A.A. Guréin-Dugué, and J.L. Schwartz Models for audiovisual fusion in a noisy-vowel recognition task Journal of VLSI Signal Processing 20 1998 25 44 (Pubitemid 128517933)
44. J.B. Tenenbaum, V. de Silva, and J.C. Langford A global geometric framework for nonlinear dimensionality reduction Science 290 12 2000 2319 2323 (Pubitemid 32041577)
45. A. Vathy-Fogarassy, and Janos Abonyib Local and global mappings of topology representing networks Information Sciences 179 21 2009 3791 3803
46. J. Wang, Z.Y. Zhang, and H.Y. Zha Adaptive manifold learning Advances in Neural Information Processing Systems 17 2005
47. K.Q. Weinberger, F. Sha, L.K. Saul, Learning a kernel matrix for nonlinear dimensionality reduction, in: International Conference on Machine Learning, 2004.
48. L. Wiskott, and T. Sejnowski Slow feature analysis: unsupervised learning of invariances Neural Computation 14 4 2002 715 770
49. J. Yang, D. Zhang, J.Y. Yang, and B. Niu Globally maximizing, locally minimizing: unsupervised discriminant projection with applications to face and palm biometrics IEEE Transactions on Pattern Analysis and Machine Intelligence 29 4 2007 650 664 (Pubitemid 46464405)
50. S.C. Yan, D. Xu, B.Y. Zhang, H.J. Zhang, Q.A. Yang, and S. Lin Graph embedding and extensions: a general framework for dimensionality reduction IEEE Transactions on Pattern Analysis and Machine Intelligence 29 1 2007 40 51 (Pubitemid 46415944)
51. H.J. Yin, and W.L. Huang Adaptive nonlinear manifolds and their applications to pattern recognition Information Sciences 180 14 2010 2649 2662
52. Z.Y. Zhang, and H.Y. Zha Principal manifolds and nonlinear dimensionality reduction via tangent space alignment SIAM Journal of Scientific Computing 26 1 2004 313 338