[1] Hua Yu and JieYang, A direct LDA algorithm for high - dimensional data with application to face recognition, Pattern Recognition Volume 34, Issue 10, October 2001,pp.2067- 2070
[2] A. Hyvarinenand E. Oja. Independent Component Analysis: Algorithms and Applications. Neural Networks, 13(4- 5):411 -430, 200
[3] J. Yang, D. Zhang, A.F. Frangi , and J.Y. Yang, Two - dimensional PCA: a new approach to appearance - based face representation and recognition, IEEE Trans. on Pattern Analysis and Machine Intelligence,
vol. 26, no. 1, pp. 131- 137, Jan. 2004
[4] R. H. David, S. Sandor and S.- T. John,Canonical correlation analysis: An overview with application to learning methods, Technical Report, CSD - TR- 03-02,2003
[5] B. Scholkopf , A. Smola , and K.R. Muller. Nonlinear component analysis as a kernel eigenvalue problem, Neural Computation, 10(5): 1299- 1319, 1998
[6] Mika, S., Ratsch , G., Weston, J., Scholkopf , B., Mullers, K.R., Fisher discriminantanalysis with kernels, Neural Networks for Signal Processing IX, Proceedings of the IEEE Signal Processing Society Workshop,
pp. 41 – 48, 1999
[7] J. B. Tenenbaum , V. de Silva, and J. C. Langford, A global geometric framework for nonlinear dimensionality reduction, Science, 290, pp. 2319 - 2323, 2000
[8] Sam T. Roweis , and Lawrence K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding,Science 22 December 2000
[9] Mikhail Belkin , Partha Niyogi ,Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , Computation , 200
[10] Xiaofei He, Partha Niyogi, Locality Preserving Projections, Advances in Neural Information Processing Systems 16 (NIPS 2003), Vancouver, Canada, 2003
