PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Multimodal Integration
Theory & Algorithms
ID Code:1190
Deposited By:Arto Klami
Deposited On:24 November 2005

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