kernelICA-icassp03
所属分类:数值算法/人工智能
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msreddyme
说明: We present a class of algorithms for independent component analysis
(ICA) which use contrast functions based on canonical correlations
in a reproducing kernel Hilbert space. On the one hand,
we show that our contrast functions are related to mutual information
and have desirable mathematical properties as measures of
statistical dependence. On the other hand, building on recent developments
in kernel methods, we show that these criteria can be
computed efficiently. Minimizing these criteria leads to flexible
and robust algorithms for ICA. We illustrate with simulations involving
a wide variety of source distributions, showing that our
algorithms outperform many of the presently known algorithms
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kernelICA-icassp03.pdf (103811, 2012-11-09)
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