A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions in .NET

Implementation qrcode in .NET A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions
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A Taxonomy of Emerging Multilinear Discriminant Analysis Solutions
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CONCLUSIONS
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This chapter provides a comprehensive introduction to the area of multilinear learning algorithms, in particular the multilinear discriminant analysis (MLDA) algorithms, for the recognition of biometric signals, most of which are naturally tensor objects. Three typical projections are introduced rst: the vector-to-vector projections (VVP), the tensor-to-tensor projections (TTP) and the tensor-to-vector projections (TVP), and two general MLDA solutions are formulated: the MLDA-TTP and the MLDA-TVP. The choices of the separation criteria and the initialization methods are then presented and the relationships between LDA, MLDA-TTP, and MLDA-TVP are discussed. A taxonomy of MLDA variants is subsequently suggested; and it not only helps us to understand the existing mutlilinear algorithms, but also bene ts us in the development of new multilinear algorithms. Finally, the MLDA variants are experimentally evaluated on the CMU PIE database and the extended Yale database B to demonstrate their performance on the popular face recognition problem. The experimental results indicate that the MLDA solutions, and multilinear learning algorithms in general, are promising emerging areas for research and applications.
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ACKNOWLEDGMENTS
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We would like to thank Cai Deng from the University of Illinois at Urbana-Champaign for making the standard-size face data available. We also would like to acknowledge all those who contributed to the research described in this chapter.
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APPENDIX: MULTILINEAR DECOMPOSITIONS
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There are two types of decompositions used most in multilinear applications: the canonical decomposition (CANDECOMP) [21, 22, 53], which is also known as the parallel factors (PARAFAC) decomposition [21, 22, 54], and the TUCKER decomposition [21, 22, 55]. With the CANDECOMP decomposition, a tensor A can be decomposed into a linear combination of P rank-1 tensors:
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(1) (2) (N) p u p u p u p ,
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(B.1)
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where P N In . With the TUCKER decomposition, a tensor A can be expressed n=1 as the product:
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P1 P2 PN
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p1 =1 p2 =1
pN =1 (1)
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(1) (2) (N) S(p1 , p2 , . . . , pN )up1 up2 upN
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= S 1 U
2 U(2) N U(N) ,
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(B.2)
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References
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T T T
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where Pn In for n = 1, . . . , N, S = A 1 U(1) 2 U(2) . . . N U(N) , and (n) (n) (n) U(n) = u1 u2 . . . uPn is an In Pn matrix with orthonormal column vectors. The CONDECOMP decomposition is in fact a special case of the TUCKER decomposition.
REFERENCES
1. L. D. Lathauwer, B. D. Moor, and J. Vandewalle, On the best rank-1 and rank-(R1 , R2 , . . . , RN ) approximation of higher-order tensors, SIAM J. Matrix Anal. Appl. 21(4):1324 1342, 2000. 2. S. Yan, D. Xu, Q. Yang, L. Zhang, X. Tang, and H. Zhang, Multilinear discriminant analysis for face recognition, IEEE Trans. Image Processing, 16(1):212 220, 2007. 3. H. Lu, J. Wang, and K. N. Plataniotis, A review on face and gait recognition: System, data and algorithms, in Advanced Signal Processing Handbook, 2nd edition, S. Stergiopoulos, editor, CRC Press, Boca Raton, FL, 2009, 000 000. 4. Y.-D. Kim and S. Choi, Color face tensor factorization and slicing for illumination-robust recognition, in Proceedings of the International Conference. on Biometrics, August 2007, pp. 19 28. 5. K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing and Applications, SpringerVerlag, Berlin, 2000. 6. K. W. Bowyer, K. Chang, and P. Flynn, A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition, Comput. Vis. Image Understanding 101(1):1 15, 2006. 7. S. Z. Li, C. Zhao, X. Zhu, and Z. Lei, 3D + 2D face recognition by fusion at both feature and decision levels, in Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, October 2005. 8. C. Liu and H. Wechsler, Independent component analysis of gabor features for face recognition, IEEE Trans. Neural Networks 14(4):919 928, 2003. 9. R. Chellappa, A. Roy-Chowdhury, and S. Zhou, Recognition of Humans and Their Activities Using Video, Morgan & Claypool Publishers, San Rafael, CA, 2005. 10. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, A layered deformable model for gait analysis, in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition, April 2006, pp. 249 254. 11. N. V. Boulgouris, D. Hatzinakos, and K. N. Plataniotis, Gait recognition: a challenging signal processing technology for biometrics, IEEE Signal Processing Mag. 22(6):000 000, 2005. 12. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, A full-body layered deformable model for automatic model-based gait recognition, EURASIP Journal on Advances in Signal Processing: Special Issue on Advanced Signal Processing and Pattern Recognition Methods for Biometrics, Vol. 2008, 2008, article ID 261317, 13 pages, doi:10.1155/2008/261317. 13. Z. Lei, R. Chu, R. He, S. Liao, and S. Z. Li, Face recognition by discriminant analysis with gabor tensor representation, in Proceedings of the. International Conference on Biometrics, August 2007, pp. 87 95. 14. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, MPCA: Multilinear principal component analysis of tensor objects, IEEE Trans. Neural Networks, 19(1):18 39, 2008. 15. G. Shakhnarovich and B. Moghaddam, Face recognition in subspaces, in S. Z. Li and A. K. Jain, editors, Handbook of Face Recognition, Springer-Verlag, Berlin, 2004, pp. 141 168. 16. J. Zhang, S. Z. Li, and J. Wang, Manifold learning and applications in recognition, in Y. P. Tan, K. H. Yap, and L. Wang, editors, Intelligent Multimedia Processing with Soft Computing, Springer-Verlag, Berlin, 2004, pp. 281 300. 17. M. H. C. Law and A. K. Jain, Incremental nonlinear dimensionality reduction by manifold learning, IEEE Trans. Pattern Anal. Mach. Intell. 28(3):377 391, 2006. 18. H. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos, Gait recognition through MPCA plus LDA, in Proceedings of the Biometrics Symposium 2006, September 2006, pp. 1 6, doi:10.1109/ BCC.2006.4341613.