A note on variational Bayesian factor analysis
Neural Networks
Variational Bayesian mixture of robust CCA models
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Bayesian Canonical correlation analysis
The Journal of Machine Learning Research
Stochastic variational inference
The Journal of Machine Learning Research
Bayesian sparse partial least squares
Neural Computation
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As a dimension reduction algorithm, canonical correlation analysis (CCA) encounters the issue of selecting the number of canonical correlations. In this letter, we present a Bayesian model selection algorithm for CCA based on a probabilistic interpretation. A hierarchical Bayesian model is applied to probabilistic CCA and learned by variational approximation. This method not only estimates the model parameters, but also automatically determines the number of canonical correlations and avoids overfitting. Experiments show that it performs better compared with maximum likelihood and some other model selection methods