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Title of Journal: Pattern Anal Applic

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Abbravation: Pattern Analysis and Applications

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Springer London

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DOI

10.1002/bbpc.19981021225

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1433-755X

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Topographic Bernoulli block mixture mapping for bi

Authors: Rodolphe Priam Mohamed Nadif Gérard Govaert
Publish Date: 2014/05/21
Volume: 17, Issue: 4, Pages: 839-847
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Abstract

Coclustering methods are valuable parsimonious approaches for the analysis of a binary data table by a simultaneous partitioning of the rows or the columns Bringing the property of visualization to coclustering is of first importance for a fast access to the essential topics and their relations We propose a new generative selforganizing map by a particular parameterization of the Bernoulli block mixture model The method is called block GTM or topographic block model Thanks to the underlying probabilistic framework the inference of the parameters of the method is performed with the block EM algorithm At the maximization step two local quadratic approximations of the objective function arise from a secondorder optimization respectively with the Newton–Raphson algorithm and with a variational bound of the sigmoid function In the experiments with several datasets the two algorithms are able to outperform former approaches and lead to similar results when the parameters are regularized with a L 1norm The conclusion summarizes the contribution and some perspectives

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