Abstract
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.
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Mashrgy, M.A., Bouguila, N., Daoudi, K. (2011). A Robust Approach for Multivariate Binary Vectors Clustering and Feature Selection. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24958-7_15
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DOI: https://doi.org/10.1007/978-3-642-24958-7_15
Publisher Name: Springer, Berlin, Heidelberg
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