PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Agglomerative Independent Variable Group Analysis
Antti Honkela, Jeremias Seppä and Esa Alhoniemi
In: 15th European Symposium on Artificial Neural Networks (ESANN 2007), 25-27 Apr 2007, Bruges, Belgium.

Abstract

Independent Variable Group Analysis (IVGA) is a principle for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate groups. In this paper an agglomerative method for learning a hierarchy of IVGA groupings is presented. The method resembles hierarchical clustering, but the distance measure is 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 ease construction of a predictive model.

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EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:3234
Deposited By:Antti Honkela
Deposited On:27 January 2008