文件名称:kernelICA-jmlr
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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
and their derivatives can be computed e±ciently. Minimizing these criteria leads to °exible
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.
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
and their derivatives can be computed e±ciently. Minimizing these criteria leads to °exible
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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