Non-parametric dependent components
Arto Klami and Samuel Kaski
In: ICASSP'05, IEEE International Conference on Acoustics, Speech, and Signal Processing, 18-23 Mar 2005, Philadelphia, USA.
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.