A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection

Mohamed Al Mashrgy 1 Nizar Bouguila 1 Khalid Daoudi 2
1 CIISE - Concordia Institute for Information Systems Engineering
2 GeoStat - Geometry and Statistics in acquisition data
Inria Bordeaux - Sud-Ouest
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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Communication dans un congrès
International Conference on Neural Information Processing (ICONIP), Nov 2011, Shanghai, China. 2011
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Dernière modification le : mercredi 29 novembre 2017 - 15:10:59
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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. 2011. 〈hal-00647989〉

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