Model selection for gaussian kernel PCA denoising

IEEE Transactions on Neural Networks and Learning Systems

Volumn 23, Issue 1, 2012, Pages 163-168

  Jorgensen, Kasper Winther ^a,b   Hansen, Lars Kai ^a  

^a TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^b Mental Health Center Glostrup   (Denmark)

Author keywords

Denoising; kernel principal component analysis; model selection; parallel analysis

Indexed keywords

DE-NOISING; HANDWRITTEN DIGIT; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); MODEL SELECTION; MODEL-ORDER SELECTION; PARALLEL ANALYSIS; PERMUTATION TESTS; RADIAL BASIS FUNCTIONS;

PRINCIPAL COMPONENT ANALYSIS; RADIAL BASIS FUNCTION NETWORKS;

GAUSSIAN DISTRIBUTION;

EID: 84876897870     PISSN: 2162237X     EISSN: 21622388     Source Type: Journal    
DOI: 10.1109/TNNLS.2011.2178325     Document Type: Article

References (14)

1. A. Teixeira, A. Tomé, K. Stadlthanner, and E. W. Lang, "KPCA denoising and the pre-image problem revisited," Digital Signal Process., vol. 18, no. 4, pp. 568-580, Jul. 2008.
2. J. T. Y. Kwok and I. W. H. Tsang, "The pre-image problem in kernel methods," IEEE Trans. Neural Netw., vol. 15, no. 6, pp. 1517-1525, Nov. 2004.
3. S. Mika, B. Schölkopf, A. Smola, K.-R. Müller, M. Scholz, and G. Rätsch, "Kernel PCA and de-noising in feature spaces," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 1999.
4. J. J. Hull, "A database for handwritten text recognition research," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 5, pp. 550-554, May 1994.
5. P. Arias, G. Randall, and G. Sapiro, "Connecting the out-of-sample and pre-image problems in kernel methods," in Proc. IEEE Conf. Comput. Vision Pattern Recognit., Minneapolis, MN, Jun. 2007, pp. 1-8.
6. N. Thorstensen, F. Segonne, and R. Keriven, "Normalization and preimage problem in Gaussian kernel PCA," in Proc. IEEE Image Process. 15th Int. Conf., San Diego, CA, Oct. 2008, pp. 741-744.
7. K. A. Yeomans and P. A. Golder, "The Guttman-Kaiser criterion as a predictor of the number of common factors," J. Royal Stat. Soc. Ser. D: Stat., vol. 31, no. 3, pp. 221-229, 1982.
8. L. K. Hansen, J. Larsen, F. A. Nielsen, S. C. Strother, E. Rostrup, R. Savoy, N. Lange, J. Sidtis, C. Svarer, and O. B. Paulson, "Generalizable patterns in neuroimaging: How many principal components?" NeuroImage, vol. 9, no. 5, pp. 534-544, May 1999.
9. C. Alzate and J. Suykens, "Kernel component analysis using an epsiloninsensitive robust loss function," IEEE Trans. Neural Netw., vol. 19, no. 9, pp. 1583-1598, Sep. 2008.
10. J. L. Horn, "A rationale and test for the number of factors in factor analysis," Psychometrika, vol. 30, no. 2, pp. 179-185, 1965.
11. R. D. Ledesma and P. Valero-Mora, "Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out parallel analysis," Pract. Assess. Res. Eval., vol. 12, no. 2, pp. 1-11, 2007.
12. A. Beauducel, "Problems with parallel analysis in data sets with oblique simple structure," Methods Psychol. Res., vol. 6, no. 2, pp. 141-157, 2001.
13. B. Schölkopf, A. Smola, and K.-R. Müller, "Nonlinear component analysis as a kernel eigenvalue problem," Neural Comput., vol. 10, no. 5, pp. 1299-1319, Jul. 1998.
14. J. Baglama, D. Calvetti, and L. Reichel, "Iterative methods for the computation of a few eigenvalues of a large symmetric matrix," BIT Numerical Math., vol. 36, no. 3, pp. 400-421, 1996.