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.