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JACIII Vol.15 No.7 pp. 800-805
doi: 10.20965/jaciii.2011.p0800
(2011)

Paper:

Dependence-Maximization Clustering with Least-Squares Mutual Information

Manabu Kimura and Masashi Sugiyama

Department of Computer Science, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan

Received:
February 18, 2011
Accepted:
May 3, 2011
Published:
September 20, 2011
Keywords:
dependence-maximization clustering, squared-loss mutual information, least-squares mutual information, model selection, kernel
Abstract

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering. In this paper, we follow this line of research and propose a novel dependence-maximization clustering method based on least-squares mutual information, which is an estimator of a squared-loss variant of mutual information. A notable advantage of the proposed method over existing approaches is that hyperparameters such as kernel parameters and regularization parameters can be objectively optimized based on cross-validation. Thus, subjective manual-tuning of hyperparameters is not necessary in the proposed method, which is a highly useful property in unsupervised clustering scenarios. Through experiments, we illustrate the usefulness of the proposed approach.

Cite this article as:
Manabu Kimura and Masashi Sugiyama, “Dependence-Maximization Clustering with Least-Squares Mutual Information,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.7, pp. 800-805, 2011.
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