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
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.
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