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Summary: Extreme Components Analysis
Max Welling
Department of Computer Science
University of Toronto
10 King's College Road
Toronto, M5S 3G5 Canada
welling@cs.toronto.edu
Felix Agakov, Christopher K. I. Williams
Institute for Adaptive and Neural Computation
School of Informatics
University of Edinburgh
5 Forrest Hill, Edinburgh EH1 2QL, UK
fckiw,felixag@inf.ed.ac.uk
Abstract
Principal components analysis (PCA) is one of the most widely used
techniques in machine learning and data mining. Minor components
analysis (MCA) is less well known, but can also play an important role
in the presence of constraints on the data distribution. In this paper we
present a probabilistic model for ``extreme components analysis'' (XCA)
which at the maximum likelihood solution extracts an optimal combina
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