Abstract : We propose an infinite mixture model for the clustering of positive data. The proposed model is based on the generalized inverted Dirichlet distribution which has a more general covariance structure than the inverted Dirichlet that has been widely used recently in several machine learning and data mining applications. The proposed mixture is developed in an elegant way that allows simultaneous clustering and feature selection, and is learned using a fully Bayesian approach via Gibbs sampling. The merits of the proposed approach are demonstrated using a challenging application namely images categorization.
https://hal.inria.fr/hal-01397226
Contributor : Hal Ifip
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Submitted on : Tuesday, November 15, 2016 - 3:47:45 PM
Last modification on : Thursday, July 26, 2018 - 2:08:02 PM
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Nizar Bouguila, Mohamed Mashrgy. An Infinite Mixture Model of Generalized Inverted Dirichlet Distributions for High-Dimensional Positive Data Modeling. 2nd Information and Communication Technology - EurAsia Conference (ICT-EurAsia), Apr 2014, Bali, Indonesia. pp.296-305, ⟨10.1007/978-3-642-55032-4_29⟩. ⟨hal-01397226⟩
