Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Lecture Notes in Electrical Engineering |
| Publisher | Springer Verlag |
| Pages | 651-660 |
| Number of pages | 10 |
| DOIs | |
| Publication status | Published - 2016 Jan 1 |
Publication series
| Name | Lecture Notes in Electrical Engineering |
|---|---|
| Volume | 375 |
| ISSN (Print) | 1876-1100 |
| ISSN (Electronic) | 1876-1119 |
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All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
Cite this
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A variational bayesian approach for unsupervised clustering. / Chen, Mu Song; Wang, Hsuan Fu; Hwang, Chipan; Ho, Tze Yee; Hung, Chan Hsiang.
Lecture Notes in Electrical Engineering. Springer Verlag, 2016. p. 651-660 (Lecture Notes in Electrical Engineering; Vol. 375).Research output: Chapter in Book/Report/Conference proceeding › Chapter
TY - CHAP
T1 - A variational bayesian approach for unsupervised clustering
AU - Chen, Mu Song
AU - Wang, Hsuan Fu
AU - Hwang, Chipan
AU - Ho, Tze Yee
AU - Hung, Chan Hsiang
PY - 2016/1/1
Y1 - 2016/1/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=85007571997&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-0539-8_63
DO - 10.1007/978-981-10-0539-8_63
M3 - Chapter
T3 - Lecture Notes in Electrical Engineering
SP - 651
EP - 660
BT - Lecture Notes in Electrical Engineering
PB - Springer Verlag
ER -