JACIII Vol.19 No.6 pp. 818-824
doi: 10.20965/jaciii.2015.p0818


On the Optimal Hyperparameter Behavior in Bayesian Clustering

Keisuke Yamazaki

Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology
G5-19, 4259 Nagatsuta, Midori-ku, Yokohama 226-8503, Japan

May 20, 2015
August 18, 2015
November 20, 2015
cluster analysis, Bayes statistics, unsupervised learning, asymptotic analysis
In a probabilistic approach to cluster analysis, parametric models, such as a mixture of Gaussian distributions, are often used. Since the parameter is unknown, it is necessary to estimate both the parameter and the labels of the clusters. Recently, the statistical properties of Bayesian clustering have been studied. The theoretical accuracy of the label estimation has been analyzed, and it has been found to be better than the maximum-likelihood method, which is based on the expectation-maximization algorithm. However, the effect of a prior distribution on the clustering result remains unknown. The prior distribution has the parameter, which is the hyperparameter. In the present paper, we theoretically and experimentally investigate the behavior of the optimal hyperparameter, and we propose an evaluation method for the clustering result, based on the prior optimization.
Cite this article as:
K. Yamazaki, “On the Optimal Hyperparameter Behavior in Bayesian Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.19 No.6, pp. 818-824, 2015.
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