Improving The Robustness To Outliers Of Mixtures Of Probabilistic Pcas

Principal Component Analysis, when formulated as a probabilistic model, can be made robust to outliers by using a Student-t assumption on the noise distribution instead of a Gaussian one. On the other hand, mixtures of PCA is a model aimed to discover nonlinear dependencies in data by finding clusters and identifying local linear sub-manifolds. This paper shows how mixtures of PCA can be made robust to outliers too. Using a hierarchical probabilistic model, parameters are set by likelihood maximization. The method is shown to be effectively robust to outliers, even in the context of high-dimensional data.