Kernel independent component analysis


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.

DOI: 10.1109/ICASSP.2003.1202783

Extracted Key Phrases

2 Figures and Tables

Citations per Year

1,446 Citations

Semantic Scholar estimates that this publication has 1,446 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Bach2002KernelIC, title={Kernel independent component analysis}, author={Francis R. Bach and Michael I. Jordan}, booktitle={ICASSP}, year={2002} }