Robust multilinear principal component analysis

Proceedings of the IEEE International Conference on Computer Vision

Volumn , Issue , 2009, Pages 591-597

  Inoue, Kohei ^a   Hara, Kenji ^a   Urahama, Kiichi ^a  

^a KYUSHU UNIVERSITY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; HIGHER-ORDER TENSOR; ITERATIVE ALGORITHM;

ALGORITHMS; COMPUTER VISION; LAGRANGE MULTIPLIERS; TENSORS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 77953191395     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2009.5459186     Document Type: Conference Paper

References (25)

1. B. W. Bader and T. G. Kolda. Algorithm 862: Matlab tensor classes for fast algorithm prototyping. ACM Trans. Math. Softw., 32(4):635-653, December 2006. 2, 3, 4
2. D. Cai, X. He, and J. Han. Subspace learning based on tensor analysis. Department of Computer Science Technical Report No. 2572, University of Illinois at Urbana-Champaign (UIUCDCS-R-2005-2572), May 2005. 1
3. D. Comaniciu. An algorithm for data-driven bandwidth selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(2):281-288, 2003. 5
4. D. Cremers, T. Kohlberger, and C. Schnörr. Shape statistics in kernel space for variational image segmentation. Pattern Recognition, 36(9):1929-1943, September 2003. 1
5. F. De la Torre and M. J. Black. Robust principal component analysis for computer vision. In Proc. Int. Conf. on Computer Vision, volume 1, pages 362-369, 2001. 1
6. F. De la Torre and M. J. Black. A framework for robust subspace learning. International Journal of Computer Vision, 54(1):117-142, 2003. 1
7. L. De Lathauwer, B. De Moor, and J. Vandewalle. A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications, 21(4):1253-1278, 2000. 2
8. R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification (2nd Edition). Wiley-Interscience, November 2000.1
9. Q.-x. Gao. Is two-dimensional pca equivalent to a special case of modular pca? Pattern Recogn. Lett., 28(10):1250-1251, 2007. 1
10. F. R. Hampel, E. M. Ronchetti, P. J. Rousseeuw, and W. A. Stahel. Robust Statistics: The Approach Based on Influence Functions (Wiley Series in Probability and Statistics). Wiley-Interscience, New York, revised edition, April 2005. 2
11. I. Higuchi and S. Eguchi. Robust principal component analysis with adaptive selection for tuning parameters. Journal of Machine Learning Research, 5:453-471, 2004. 1
12. I. T. Jolliffe. Principal Component Analysis. Springer, second edition, October 2002. 1
13. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos. Mpca: Multilinear principal component analysis of tensor objects. IEEE Transactions on Neural Networks, 19(1):18-39, 2008. 1, 2
14. H. Lütkepohl. Handbook of Matrices. John Wiley & Sons Inc, October 1996. 4
15. F. Samaria and A. Harter. Parameterisation of a stochastic model for human face identification. In Proc. 2nd IEEE Workshop on Applications of Computer Vision, 1994. 5
16. B. Savas and L. Eldén. Handwritten digit classification using higher order singular value decomposition. Pattern Recogn., 40(3):993-1003, 2007. 1
17. J. Schmid and N. Magnenat-Thalmann. Mri bone segmentation using deformable models and shape priors. In MICCAI '08: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I, pages 119-126, Berlin, Heidelberg, 2008. Springer-Verlag. 1
18. M. B. Stegmann and D. D. Gomez. A brief introduction to statistical shape analysis, mar 2002. Images, annotations and data reports are placed in the enclosed zip-file. 1
19. L. Wang, X. Wang, X. Zhang, and J. Feng. The equivalence of two-dimensional pca to line-based pca. Pattern Recogn. Lett., 26(1):57-60, 2005. 1
20. L. Xu and A. L. Yuille. Robust principal component analysis by self-organizing rules based on statistical physics approach. IEEE Transactions on Neural Networks, 6(1):131-143, 1995. 1
21. J. Yang, D. Zhang, A. Frangi, and J. yu Yang. Two-imensional pca: a new approach to appearance-based face representation and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(1):131-137, 2004. 1
22. J. Ye. Generalized low rank approximations of matrices. Machine Learning, 61(1-3):167-191, November 2005. 1
23. J. Ye, R. Janardan, and Q. Li. Gpca: an efficient dimension reduction scheme for image compression and retrieval. In KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 354-363, New York, NY, USA, 2004. ACM Press. 1
24. L. Zelnik-Manor and P. Perona. Self-tuning spectral clustering. In L. K. Saul, Y.Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems 17, pages 1601-1608. MIT Press, Cambridge, MA, 2005. 5
25. D. Zhang, S. Chen, and J. Liu. Representing image matrices: Eigenimages versus eigenvectors. In LNCS, Advances in Neural Networks - ISNN 2005, volume 3497, pages 659-664. Springer Berlin / Heidelberg, 2005. 1