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Conference Papers Year : 2011

A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection

Abstract

Given a set of binary vectors drawn from a ¯nite multiple Bernoulli mixture model, an important problem is to determine which vectors are outliers and which features are relevant. The goal of this paper is to propose a model for binary vectors clustering that accommo- dates outliers and allows simultaneously the incorporation of a feature selection methodology into the clustering process. We derive an EM al- gorithm to ¯t the proposed model. Through simulation studies and a set of experiments involving handwritten digit recognition and visual scenes categorization, we demonstrate the usefulness and e®ectiveness of our method.
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Dates and versions

hal-00647989 , version 1 (04-12-2011)

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  • HAL Id : hal-00647989 , version 1

Cite

Mohamed Al Mashrgy, Nizar Bouguila, Khalid Daoudi. A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection. International Conference on Neural Information Processing (ICONIP), Nov 2011, Shanghai, China. ⟨hal-00647989⟩

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