Kernel Independent Component Analysis
Francis R. Bach, Michael I. Jordan;
3(Jul):1-48, 2002.
Abstract
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 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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