Coupled principal component analysis

IEEE Transactions on Neural Networks

Volumn 15, Issue 1, 2004, Pages 214-222

  Möller, Ralf ^a   Könies, Axel ^b  

^a MAX PLANCK INSTITUTE FOR HUMAN COGNITIVE AND BRAIN SCIENCES   (Germany)

^b MAX PLANCK INSTITUT FÜR PLASMAPHYSIK   (Germany)

Author keywords

Adaptive learning algorithm; Minor component analysis (MCA); Neural networks; Newton's method; Oja's rule; Principal component analysis (PCA); Robust recursive least squares learning algorithm; Speed stability tradeoff

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; DIFFERENTIAL EQUATIONS; EIGENVALUES AND EIGENFUNCTIONS; KNOWLEDGE BASED SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; MATRIX ALGEBRA; OPTIMIZATION; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

ADAPTIVE LEARNING ALGORITHM; LEARNING RULES; MINOR COMPONENT ANALYSIS; NEWTON METHOD; OJA RULE; RECURSIVE LEAST SQUARES LEARNING ALGORITHM; SPEED STABILITY TRADEOFF;

NEURAL NETWORKS;

LETTER; PRINCIPAL COMPONENT ANALYSIS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 6044229348     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2003.820439     Document Type: Letter

References (16)

1. E. Oja, "A simplified neuron model as principal component analyzer," J. Math. Biol., vol. 15, pp. 267-273, 1982.
2. T. D. Sanger, "Optimal unsupervised learning in a single-layer linear feedforward neural network," Neural Networks, vol. 2, no. 6, pp. 459-473, 1989.
3. K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks. Theory and Applications. New York: Wiley, 1996.
4. G. Cirrincione, M. Cirrincione, J. Hèrault, and S. van Huffel, "The MCA EXIN neuron for the minor component analysis," IEEE Trans. Neural Networks, vol. 13, pp. 160-186, Jan. 2002.
5. S. Ouyang, Z. Bao, and G.-S. Liao, "Robust recursive least squares algorithm for principal component analysis," IEEE Trans. Neural Networks, vol. 11, pp. 218-221, Jan. 2000.
6. L.-H. Chen and S. Chang, "An adaptive learning algorithm for principal component analysis," IEEE Trans. Neural Networks, vol. 6, pp. 1255-1263, Sept. 1995.
7. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
8. K. K. Sung and T. Poggio, "Example-based learning for view-based human face detection," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 39-51, 1998.
9. D. Z. Feng, Z. Bao, and L. C. Jiao, "Total least mean squares algorithm," IEEE Trans. Signal Processing, vol. 46, pp. 2122-2130, Aug. 1998.
10. K. Hornik and C.-M. Kuan, "Convergence analysis of local feature extraction algorithms," Neural Networks, vol. 5, no. 2, pp. 229-240, 1992.
11. S. Ouyang, Z. Bao, and G. Liao, "Adaptive step-size minor component extraction algorithm," Electron. Lett., vol. 35, no. 6, pp. 443-444, Mar. 1999.
12. S. Albrecht, J. Busch, M. Kloppenburg, F. Metze, and P. Tavan, "Generalized radial basis function networks for classification and novelty detection: Self-organization of optimal bayesian decision," Neural Networks, vol. 13, no. 10, pp. 1075-1093, 2000.
13. S. A. Nene, S. K. Nayar, and H. Murase, (1996) Columbia Object Image Library (COIL-20) Tech. Rep. CUCS-006-96. Dept. Comput. Sci., Columbia Univ., New York. [Online]. Available: http://www1.cs.columbia.edu/CAVE/research/softlib/coil-20.html
14. ____, (1994) Software Library for Appearance Matching (SLAM) Tech. Rep. CUCS-019-94. Dept. Comput. Sci., Columbia Univ., New York. [Online]. Available: http://www1.cs.columbia.edu/CAVE/research/softlib/slam.html
15. S. K. Nayar, H. Murase, and S. A. Nene, "Parametric appearance representation," in Early Visual Learning, S. K. Nayar and T. Poggio, Eds, U.K.: Oxford Univ. Press, 1996, ch. 6, pp. 131-160.
16. S. Bannour and M. R. Azimi-Sadjadi, "Principal component extraction using recursive least squares learning," IEEE Trans. Neural Networks, vol. 6, pp. 457-469, Mar. 1995.