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ESANN
2007

Agglomerative Independent Variable Group Analysis

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Agglomerative Independent Variable Group Analysis
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. Key words: hierarchical clustering, independent variable group analysis, mutual information, variable grouping, variational Bayesian learning
Antti Honkela, Jeremias Seppä, Esa Alhoniemi
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2007
Where ESANN
Authors Antti Honkela, Jeremias Seppä, Esa Alhoniemi
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