Bayesian Canonical Correlation Analysis
Bayesian canonical correlation analysis is a probabilistic generative model that identifies shared latent structure between two or more sets of observed variables. It extends classical CCA by placing priors on model parameters, enabling principled uncertainty quantification, automatic determination of the number of shared dimensions, and robustness when sample sizes are small relative to dimensionality.
Source record
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
- Bach, F. R. & Jordan, M. I. (2005). A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley. · URL
- Klami, A., Virtanen, S. & Kaski, S. (2013). Bayesian canonical correlation analysis. Journal of Machine Learning Research, 14, 965-1003. · URL
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Related methods
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