PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Compact Modeling of Data Using Independent Variable Group Analysis
Esa Alhoniemi, Antti Honkela, Krista Lagus, Jeremias Seppä, Paul Wagner and Harri Valpola
IEEE Transactions on Neural Networks Volume 18, Number 6, pp. 1762-1776, 2007. ISSN 1045-9227

Abstract

We introduce a modeling approach called independent variable group analysis (IVGA) which can be used for finding an efficient structural representation for a given data set. The basic idea is to determine such a grouping for the variables of the data set that mutually dependent variables are grouped together whereas mutually independent or weakly dependent variables end up in separate groups. Computation of an IVGA model requires a combinatorial algorithm for grouping of the variables and a modeling algorithm for the groups. In order to be able to compare different groupings, a cost function which reflects the quality of a grouping is also required. Such a cost function can be derived, for example, using the variational Bayesian approach, which is employed in our study. This approach is also shown to be approximately equivalent to minimizing the mutual information between the groups. The modeling task is computationally demanding. We describe an efficient heuristic grouping algorithm for the variables and derive a computationally light nonlinear mixture model for modeling of the dependencies within the groups. Finally, we carry out a set of experiments which indicate that IVGA may turn out to be beneficial in many different applications.

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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:3235
Deposited By:Antti Honkela
Deposited On:27 January 2008