A subspace co-training framework for multi-view clustering

This paper addresses the problem of unsupervised clustering with multi-view data of high dimensionality. We propose a new algorithm which learns discriminative subspaces in an unsupervised fashion based upon the assumption that a reliable clustering should assign same-class samples to the same cluster in each view. The framework combines the simplicity of k-means clustering and Linear Discriminant Analysis (LDA) within a co-training scheme which exploits labels learned automatically in one view to learn discriminative subspaces in another. The effectiveness of the proposed algorithm is demonstrated empirically under scenarios where the conditional independence assumption is either fully satisfied (audio-visual speaker clustering) or only partially satisfied (handwritten digit clustering and document clustering). Significant improvements over alternative multi-view clustering approaches are reported in both cases. The new algorithm is flexible and can be readily adapted to use different distance measures, semi-supervised learning, and non-linear problems.