Probabilistic Disjoint Principal Component Analysis

One of the most relevant problems in Principal Component Analysis and Factor Analysis is the interpretation of the components/factors. In this paper, Disjoint Principal Component Analysis model is extended in a maximum likelihood framework to allow for inference on the model parameters. A coordinate ascent algorithm is proposed to estimate the model parameters. The performance of the methodology is evaluated on simulated and real data sets