Covariance-Guided Mixture Probabilistic Principal Component Analysis (C-MPPCA)

However, a single two-dimensional visualization is often insufficient for capturing all or most interesting structures in complex high-dimensional datasets. For this reason, Tipping and Bishop developed mixture probabilistic principal component analysis (MPPCA) that separates data into multiple groups and enables a unique projection per group; that is, one probabilistic principal component analysis (PPCA) data visualization per group.

Because the group labels are assigned to observations based on their high-dimensional coordinates, MPPCA works well to reveal homoscedastic structures in data that differ spatially. In the presence of heteroscedasticity, however, MPPCA may still mask noteworthy data structures. We propose a new method called covariance-guided MPPCA (C-MPPCA) that groups subsets of observations based on covariance, not locality, and, similar to MPPCA, displays them using PPCA.