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02-02-2021 | Methodologies and Application

Bayesian inference for infinite asymmetric Gaussian mixture with feature selection

Authors: Ziyang Song, Samr Ali, Nizar Bouguila

Published in: Soft Computing | Issue 8/2021

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Abstract

Data clustering is a fundamental unsupervised learning approach that impacts several domains such as data mining, computer vision, information retrieval, and pattern recognition. In this work, we develop a statistical framework for data clustering which uses Dirichlet processes and asymmetric Gaussian distributions. The parameters of this framework are learned using Markov Chain Monte Carlo inference approaches. We also integrate a feature selection technique to choose the features that are most informative in order to construct an appropriate model in terms of clustering accuracy. This paper reports results based on experiments that concern dynamic textures clustering as well as scene categorization.

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Title: Bayesian inference for infinite asymmetric Gaussian mixture with feature selection
Authors: Ziyang Song
Samr Ali
Nizar Bouguila
Publication date: 02-02-2021
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 8/2021
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-021-05598-4

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