PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Agglomerative independent variable group analysis
Antti Honkela, Jeremias Seppä and Esa Alhoniemi
Neurocomputing Volume 71, Number 7-9, pp. 1311-1320, 2008. ISSN 0925-2312

Abstract

Independent variable group analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper two variants of an agglomerative method for learning a hierarchy of IVGA groupings are presented. The method resembles hierarchical clustering, but the choice of clusters to merge is based on variational Bayesian model comparison. This is approximately equivalent to using a distance measure based on a model-based approximation of mutual information between groups of variables. The approach also allows determining optimal cutoff points for the hierarchy. The method is demonstrated to find sensible groupings of variables that can be used for feature selection and ease construction of a predictive model.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4957
Deposited By:Antti Honkela
Deposited On:24 March 2009