Agglomerative independent variable group analysis

doi:10.1016/j.neucom.2007.11.024

2008-03-01

Antti Honkela *

Jeremias Seppä

Esa Alhoniemi

DOI：10.1016/j.neucom.2007.11.024

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摘要

En 中文

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 oil 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. (c) 2008 Elsevier B.V. All rights reserved.

Keyword:

hierarchical clustering

independent variable group analysis

mutual information

variable grouping

variational Bayesian learning

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期刊

Neurocomputing

IF:

6.5

论文数:

2.5W

被引数:

6.5W

机构

Aalto University

学者数:

1.6W

论文数: 1.5W

被引数: 2.1W

University of Turku

学者数:

1.7W

论文数: 1.5W

被引数: 2.0W

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