Information-Maximization Clustering based on Squared-Loss Mutual Information

Information-maximization clustering learns a probabilistic classifier in an unsupervised manner so that mutual information between feature vectors and cluster assignments is maximized. A notable advantage of this approach is that it only involves continuous optimization of model parameters, which is substantially easier to solve than discrete optimization of cluster assignments. However, existing methods still involve non-convex optimization problems, and therefore finding a good local optimal solution is not straightforward in practice. In this paper, we propose an alternative information-maximization clustering method based on a squared-loss variant of mutual information. This novel approach gives a clustering solution analytically in a computationally efficient way via kernel eigenvalue decomposition. Furthermore, we provide a practical model selection procedure that allows us to objectively optimize tuning parameters included in the kernel function. Through experiments, we demonstrate the usefulness of the proposed approach

A semi-supervised feature clustering algorithm with application to word sense disambiguation

In this paper we investigate an application of feature clustering for word sense disambiguation, and propose a semisupervised feature clustering algorithm. Compared with other feature clustering methods (ex. supervised feature clustering), it can infer the distribution of class labels over (unseen) features unavailable in training data (labeled data) by the use of the distribution of class labels over (seen) features available in training data. Thus, it can deal with both seen and unseen features in feature clustering process. Our experimental results show that feature clustering can aggressively reduce the dimensionality of feature space, while still maintaining state of the art sense disambiguation accuracy. Furthermore, when combined with a semi-supervised WSD algorithm, semi-supervised feature clustering outperforms other dimensionality reduction techniques, which indicates that using unlabeled data in learning process helps to improve the performance of feature clustering and sense disambiguation.