Non-Parametric Dependent Components
2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING(2005)
摘要
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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关键词
bayesian methods,bayes factors,normal distribution,stress,distributed computing,stress analysis,mutual information,canonical correlation analysis,cost function,computer science,information analysis
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