Arto Klami and Samuel Kaski. Non-parametric dependent components. In Proceedings of ICASSP'05, IEEE International Conference on Acoustics, Speech, and Signal Processing, pages V-209 -V-212, IEEE, 2005. (pdf)

Canonical correlation analysis (CCA) is equivalent to finding mutual information-maximizing projections for normally distributed data. We remove the restriction of normality by non-parametric estimation, and formulate the problem of finding dependent components with a connection to Bayes factors. The method is applied for characterizing yeast stress by finding what is in common in several different stress conditions.

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