Patch alignment for dimensionality reduction

IEEE Transactions on Knowledge and Data Engineering

Volumn 21, Issue 9, 2009, Pages 1299-1313

  Zhang, Tianhao ^a   Tao, Dacheng ^b   Li, Xuelong ^c   Yang, Jie ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^c UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Dimensionality reduction; Discriminative locality alignment; Patch alignment; Spectral analysis

Indexed keywords

COMMON PROPERTY; COMPUTER VISION APPLICATIONS; DIMENSIONALITY REDUCTION; DIMENSIONALITY REDUCTION ALGORITHMS; DISCRIMINATIVE ABILITY; DISCRIMINATIVE LOCALITY ALIGNMENT; EMPIRICAL STUDIES; INTRINSIC DIFFERENCES; LAPLACIAN EIGENMAPS; LOCAL TANGENT SPACE ALIGNMENT; LOCALLY LINEAR EMBEDDING; NON-LINEARITY; PATCH ALIGNMENT; SPECTRAL ANALYSIS; SYSTEMATIC FRAMEWORK; TWO STAGE;

ALGORITHMS; COMPUTER APPLICATIONS; COMPUTER VISION; MINING; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS;

ALIGNMENT;

EID: 68549120964     PISSN: 10414347     EISSN: None     Source Type: Journal    
DOI: 10.1109/TKDE.2008.212     Document Type: Article

References (40)

1. Y. Aslandogan and C. Yu, "Techniques and Systems for Image and Video Retrieval," IEEE Trans. Knowledge and Data Eng., vol. 11, no. 1, pp. 56-63, Jan./Feb. 1999.
2. P. Belhumeour, J. Hespanha, and D. Kriegman, "Eigenfaces versus Fisherfaces: Recognition Using Class Specific Linear Projection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.
3. M. Belkin and P. Niyogi, "Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering," Advances in Neural Information Processing System. vol. 14, pp. 585-591, MIT Press, 2002.
4. R. Bellman, Adaptive Control Processes: A Guided Tour. Princeton Univ. Press, 1961.
5. Y. Bengio, J. Paiement, P. Vincent, O. Dellallaeu, L. Roux, and M. Quimet, "Out-of Sample Extensions for LLE, Isomap, MDS, Eigenmaps, and Spectral Clustering," Advances in Neural Information Processing System, vol. 16, pp. 177-184, MIT Press, 2004.
6. C.M. Bishop, M. Svensén, and C.K.I. Williams, "GTM: The Generative Topographic Mapping," Neural Computation, vol. 10, no. 1, pp. 215-234, 1998.
7. C.M. Bishop, M. Svensén, and C.K.I. Williams, "Developments of the Generative Topographic Mapping," Neurocomputing, vol. 21, pp. 203-224, 1998.
8. D. Cai, X. He, and J. Han, "Document Clustering Using Locality Preserving Indexing," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 12, pp. 1624-1637, Dec. 2005.
9. D. Cai, X. He, and J. Han, "SRDA: An Efficient Algorithm for Large Scale Discriminant Analysis," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 1, pp. 1-12, Jan. 2008.
10. D. Cai, X. He, and J. Han, "Using Graph Model for Face Analysis," Technical Report No. 2636, Dept. of Computer Science, Univ. of Illinois at Urbana-Champaign, Sept. 2005.
11. D.L. Donoho and C. Grimes, "Hessian Eigenmaps: New Locally Linear Embedding Techniques for High-dimensional Data," Proc. Nat'l Academy of Sciences USA, vol. 100, no. 10, pp. 5591-5596, 2003.
12. R. Duda, P. Hart, and D. Stork, Pattern Classification, second ed. Wiley, 2000.
13. R.A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems," Annals of Eugenics, vol. 7, pp. 179-188, 1936.
14. B. Gao, T. Liu, G. Feng, T. Qin, Q. Cheng, and W. Ma, "Hierarchical Taxonomy Preparation for Text Categorization Using Consistent Bipartite Spectral Graph Copartitioning," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 9, pp. 1263-1273, Sept. 2005.
15. D.B. Graham and N.M. Allinson, "Characterizing Virtual Eigen-signatures for General Purpose Face Recognition," Face Recognition: From Theory to Applications, H. Wechsler, P.J. Pillips, V. Bruce, F. Fogelman-Soulie and T.S. Huang, eds., pp. 446-456, Springer, 1998.
16. X. He, D. Cai, and J. Han, "Learning a Maximum Margin Subspace for Image Retrieval," IEEE Trans. Knowledge and Data Eng., vol. 20, no. 2, pp. 189-201, Feb. 2008.
17. X. He, D. Cai, J. Wen, W. Ma, and H. Zhang, "Clustering and Searching WWW Images Using Link and Page Layout Analysis," ACM Trans. Multimedia Computing, Comm., and Applications, vol. 3, no. 2, Article 10, 2007.
18. X. He, D. Cai, S. Yan, and H. Zhang, "Neighborhood Preserving Embedding," Proc. IEEE Int'l Conf. Computer Vision, pp. 1208-1213, 2005.
19. X. He and P. Niyogi, "Locality Preserving Projections," Advances in Neural Information Processing System, vol. 16, pp. 153-160, MIT Press, 2004.
20. H. Hotelling, "Analysis of a Complex of Statistical Variables into Principal Components," J. Educational Psychology, vol. 24, pp. 417-441, 1933.
21. I.T. Jolliffe, Principal Component Analysis, second ed. Springer-Verlag, 2002.
22. E. Kokiopoulou and Y. Saad, "Orthogonal Neighborhood Preserving Projections: A Projection-Based Dimensionality Reduction Technique," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 12, pp. 2143-2156, Dec. 2007.
23. Y. Koren and L. Carmel, "Robust Linear Dimensionality Reduction," IEEE Trans. Visualization and Computer Graphics, vol. 10, no. 4, pp. 459-470, July/Aug. 2004.
24. P.J. Phillips, H. Moon, S.A. Rizvi, and P.J. Rauss, "The FERET Evaluation Methodology for Face-Recognition Algorithms," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, Oct. 2000.
25. S. Rosenberg, The Laplacian on a Riemannian Manifold. Cambridge Univ. Press, 1997.
26. S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, pp. 2323-2326, 2000.
27. L.K. Saul and S.T. Roweis, "Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifold," J. Machine Learning Research, vol. 4, pp. 119-155, 2003.
28. L.K. Saul, K.Q. Weinberger, J.H. Ham, F. Sha, and D.D. Lee, "Spectral Methods for Dimensionality Reduction," Semisupervised Learning, O. Chapelle, B. Schoelkopf, and A. Zien, eds., MIT Press, 2006.
29. G. Shakhnarovich and B. Moghaddam, "Face Recognition in Subspaces," Handbook of Face Recognition, S.Z. Li and A.K. Jain, eds., Springer-Verlag, 2004.
30. D. Tao, X. Li, and S.J. Maybank, "Negative Samples Analysis in Relevance Feedback," IEEE Trans. Knowledge and Data Eng., vol. 19, no. 4, pp. 568-580, Apr. 2007.
31. D. Tao, X. Li, X. Wu, and S.J. Maybank, "General Tensor Discriminant Analysis and Gabor Features for Gait Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1700-1715, Oct. 2007.
32. J. Tenenbaum, V. Silva, and J. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, pp. 2319-2323, 2000.
33. M.E. Tipping and C.M. Bishop, "Probabilistic Principal Component Analysis," J. Royal Statistical Soc. B, vol. 21, no. 3, pp. 611-622, 1999.
34. M. Turk and A. Pentland, "Face Recognition Using Eigenfaces," Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 586-591, 1991.
35. D. Xu, S. Lin, S. Yan, and X. Tang, "Rank-One Projections with Adaptive Margin for Face Recognition," IEEE Trans. Systems, Man, and Cybernetics, Part B, vol. 37, no. 5, pp. 1226-1236, Oct. 2007.
36. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, and H. Zhang, "Multilinear Discriminant Analysis for Face Recognition," IEEE Trans. Image Processing, vol. 16, no. 1, pp. 212-220, Jan. 2007.
37. S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, "Graph Embedding and Extensions: A General Framework for Dimensionality Reduction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, Jan. 2007.
38. T. Zhang, J. Yang, D. Zhao, and X. Ge, "Linear Local Tangent Space Alignment and Application to Face Recognition," Neurocomputing, vol. 70, pp. 1547-1553, 2007.
39. Z. Zhang and H. Zha, "Principal Manifolds and Nonlinear Dimension Reduction via Local Tangent Space Alignment," SIAM J. Scientific Computing, vol. 26, no. 1, pp. 313-338, 2005.
40. S. Zhou, "Probabilistic Analysis of Kernel Principal Components: Mixture Modeling and Classification," Technical Report CAR-TR-993, Center for Automation Research (CfAR), 2003.