A variational bayesian approach for unsupervised clustering

Mu Song Chen, Hsuan Fu Wang, Chi Pan Hwang, Tze Yee Ho, Chan Hsiang Hung

研究成果: Chapter

2 引文 斯高帕斯(Scopus)

摘要

Gaussian Mixture Models are among the most statistically mature methods which are used to make statistical inferences as well as performing unsupervised clustering. Formally, a gaussian mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the data set. In this paper, a probabilistic clustering based on the finite mixture models of the data distribution is suggested. An important issue in the finite mixture model-based clustering approach is to select the number of mixture components of clusters. In this sense, we focus on statistical inference for finite mixture models and illustrate how the variational Bayesian approach can be used to determine a suitable number of components in the case of a mixture of Gaussian distributions.

原文English
主出版物標題Lecture Notes in Electrical Engineering
發行者Springer Verlag
頁面651-660
頁數10
DOIs
出版狀態Published - 2016

出版系列

名字Lecture Notes in Electrical Engineering
375
ISSN(列印)1876-1100
ISSN(電子)1876-1119

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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