Improving the robustness to outliers of mixtures of probabilistic PCAs

Authors:
Nicolas Delannay;Cédric Archambeau;Michel Verleysen
Affiliations:
Université catholique de Louvain, Machine Learning Group, DICE, Louvain-la-Neuve, Belgium;Centre for Comput. Statistics and Machine Learning, University College London, London, UK;Université catholique de Louvain, Machine Learning Group, DICE, Louvain-la-Neuve, Belgium
Venue:
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2008

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Abstract

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 submanifolds. 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.