PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Robust Probabilistic Projections
Cedric Archambeau, Nicolas Delannay and Michel Verleysen
In: 23rd International Conference on Machine Learning, 25-29 June 2006, Pittsburgh (PA), USA.

Abstract

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.

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EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:2375
Deposited By:Cedric Archambeau
Deposited On:22 November 2006