Given a set of binary vectors drawn from a finite 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 accommodates outliers and allows simultaneously the incorporation of a feature selection methodology into the clustering process. We derive an EM algorithm to fit the proposed model. Through simulation studies and a set of experiments involving handwritten digit recognition and visual scenes categorization, we demonstrate the usefulness and effectiveness of our method. © 2011 Springer-Verlag.
CITATION STYLE
Mashrgy, M. A., Bouguila, N., & Daoudi, K. (2011). A robust approach for multivariate binary vectors clustering and feature selection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7063 LNCS, pp. 125–132). https://doi.org/10.1007/978-3-642-24958-7_15
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