Bayesian Canonical Correlation Analysis

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Provided by: Journal of Machine Learning Research (JMLR)
Topic: Big Data
Format: PDF
Canonical Correlation Analysis (CCA) is a classical method for seeking correlations between two multivariate data sets. During the last ten years, it has received more and more attention in the machine learning community in the form of novel computational formulations and a plethora of applications. The authors review recent developments in Bayesian models and inference methods for CCA which are attractive for their potential in hierarchical extensions and for coping with the combination of large dimensionalities and small sample sizes.
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