Bayesian Sparse Principal Component Analysis
Several real-life high dimension datasets can be reasonably represented as a
linear combination of a few sparse vectors. Succinct representation of such data with a few selected variables is highly desirable for such cases. A Bayesian setup is useful because the limitation of knowing a limited number of high dimensional data points can be alleviated by well-designed domain-specific priors.
Joydeep Ghosh, his student Rajiv Khanna, and Oluwasanmi Koyejo, currently at Stanford, are developing scalable Bayesian PCA models to extract sparse components from large datasets using a novel constrained inference framework. Results obtained so far show clear superiority as compared to a large list of standard baselines. This work will be presented at AISTATS 2015.