Dependence-Maximization Clustering with Least-Squares Mutual Information

Recently, statistical dependence measures such as mutual information and kernelized covariance have been successfully applied to clustering, called dependencemaximization clustering. In this paper, we propose a novel dependencemaximization clustering method based on an estimator of a squared-loss variant of mutual information called least-squares mutual information. A notable advantage of the proposed method over existing ones 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.