Partial multi-view clustering

Abstract

Real data are often with multiple modalities or coming from multiple channels, while multi-view clustering provides a natural formulation for generating clusters from such data. Previous studies assumed that each example appears in all views, or at least there is one view containing all examples. In real tasks, however, it is often the case that every view suffers from the missing of some data and therefore results in many partial examples, i.e., examples with some views missing. In this paper, we present possibly the first study on partial multiview clustering. Our proposed approach, PVC, works by establishing a latent subspace where the instances corresponding to the same example in different views are close to each other, and similar instances (belonging to different examples) in the same view should be well grouped. Experiments on two-view data demonstrate the advantages of our proposed approach.