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.

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