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An information-theoretic perspective to kernel independent components analysis

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4 Author(s)
Jian-Wu Xu ; Dept. of Electr. & Comput. Eng., Florida Int. Univ., Miami, FL, USA ; Erdogmus, D. ; Jenssen, R. ; Principe, J.C.

In this paper, we investigate the intriguing relationship between information-theoretic learning (ITL), based on weighted Parzen window density estimator, and kernel-based learning algorithms. We prove the equivalence between kernel independent component analysis (kernel ICA) and the Cauchy-Schwartz (C-S) independence measure. This link gives a theoretical motivation for the selection of the Mercer kernel, based on density estimation. Demonstrating this equivalence requires introducing a weighted kernel density estimator, a modification of Parzen windowing. We also discuss the role of the weights in the weighted Parzen windowing and kernel ICA.

Published in:

Acoustics, Speech, and Signal Processing, 2005. Proceedings. (ICASSP '05). IEEE International Conference on  (Volume:5 )

Date of Conference:

18-23 March 2005

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