Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
