Paper Info
Title
An Efficient Model Selection for Gaussian Mixture Model in a Bayesian Framework.
Abstract
In order to cluster or partition data, we often use Expectation-and-Maximization (EM) or Variational approximation with a Gaussian Mixture Model (GMM), which is a parametric probability density function represented as a weighted sum of $\hat{K}$ Gaussian component densities. However, model selection to find underlying $\hat{K}$ is one of the key concerns in GMM clustering, since we can obtain the desired clusters only when $\hat{K}$ is known. In this paper, we propose a new model selection algorithm to explore $\hat{K}$ in a Bayesian framework. The proposed algorithm builds the density of the model order which any information criterions such as AIC and BIC basically fail to reconstruct. In addition, this algorithm reconstructs the density quickly as compared to the time-consuming Monte Carlo simulation.
Year
Venue
Field
2013
CoRR
Applied mathematics,Artificial intelligence,Cluster analysis,Mathematical optimization,Monte Carlo method,Pattern recognition,Model selection,Parametric statistics,Gaussian,Partition (number theory),Probability density function,Mixture model,Mathematics
DocType
Volume
Citations 
Journal
abs/1307.0995
1
PageRank 
References 
Authors
0.35
0
1
Name
Order
Citations
PageRank
Ji Won Yoon111223.94