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Kernel independent component analysis (2002)
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| Venue: | Journal of Machine Learning Research |
| Citations: | 464 - 24 self |
BibTeX
@ARTICLE{Bach02kernelindependent,
author = {Francis R. Bach},
title = {Kernel independent component analysis},
journal = {Journal of Machine Learning Research},
year = {2002},
volume = {3},
pages = {1--48}
}
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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 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. 1.
Keyphrases
kernel independent component analysis contrast function source distribution mutual information statistical dependence independent component analysis recent development desirable mathematical property robust algorithm kernel method wide variety reproducing kernel hilbert space canonical correlation
