Robust probabilistic projections

Principal components and canonical correlations are at the root of many exploratory data mining techniques and provide standard pre-processing tools in machine learning. Lately, probabilistic reformulations of these methods have been proposed (Roweis, 1998; Tipping & Bishop, 1999b; Bach & Jordan, 2005). They are based on a Gaussian density model and are therefore, like their non-probabilistic counterpart, very sensitive to atypical observations. In this paper, we introduce robust probabilistic principal component analysis and robust probabilistic canonical correlation analysis. Both are based on a Student-t density model. The resulting probabilistic reformulations are more suitable in practice as they handle outliers in a natural way. We compute maximum likelihood estimates of the parameters by means of the EM algorithm.

• Type: Conference
• Year: 2006
• Where: ICML
• Authors: Cédric Archambeau, Michel Verleysen, Nicolas Delannay
• Keywords: ICML 2006, Machine Learning, Probabilistic Canonical Correlation, Probabilistic Principal Component, Probabilistic Reformulations