A method for simultaneous non-Gaussian data clustering, feature selection and outliers rejection is proposed in this paper. The proposed approach is based on finite generalized Dirichlet mixture models learned within a framework including expectation-maximization updates for model parameters estimation and minimum message length criterion for model selection. Through a challenging application involving texture images discrimination, it is demonstrated that the developed procedure performs effectively in avoiding outliers and selecting relevant features. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bouguila, N., Ziou, D., & Boutemedjet, S. (2011). Simultaneous non-gaussian data clustering, feature selection and outliers rejection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6744 LNCS, pp. 364–369). https://doi.org/10.1007/978-3-642-21786-9_59
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