Rényi divergence minimization based co-regularized multiview clustering

Multiview clustering is a framework for grouping objects given multiple views, e.g. text and image views describing the same set of entities. This paper introduces co-regularization techniques for multiview clustering that explicitly minimize a weighted sum of divergences to impose coherence between per-view learned models. Specifically, we iteratively minimize a weighted sum of divergences between posterior memberships of clusterings, thus learning view-specific parameters that produce similar clusterings across views. We explore a flexible family of divergences, namely Rényi divergences for co-regularization. An existing method of probabilistic multiview clustering is recovered as a special case of the proposed method. Extensive empirical evaluation suggests improved performance over a variety of existing multiview clustering techniques as well as related methods developed for information fusion with multiview data.