Open AccessOnline nonparametric Bayesian analysis of parsimonious Gaussian mixture models and scenes clustering
Author(s) -
Zhou RiGui,
Wang Wei
Publication year - 2021
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2019-0336
Subject(s) - cluster analysis , mixture model , dirichlet process , computer science , artificial intelligence , inference , pattern recognition (psychology) , data mining , machine learning , gaussian process , bayesian inference , dirichlet distribution , nonparametric statistics , bayesian probability , gaussian , mathematics , statistics , physics , quantum mechanics , mathematical analysis , boundary value problem
The mixture model is a very powerful and flexible tool in clustering analysis. Based on the Dirichlet process and parsimonious Gaussian distribution, we propose a new nonparametric mixture framework for solving challenging clustering problems. Meanwhile, the inference of the model depends on the efficient online variational Bayesian approach, which enhances the information exchange between the whole and the part to a certain extent and applies to scalable datasets. The experiments on the scene database indicate that the novel clustering framework, when combined with a convolutional neural network for feature extraction, has meaningful advantages over other models.