Tensorized and Compressed Multi-View Subspace Clustering via Structured Constraint

Abstract

Multi-view learning has raised more and more attention in recent years. However, traditional approaches only focus on the difference while ignoring the consistency among views. It may make some views, with the situation of data abnormality or noise, ineffective in the progress of view learning. Besides, the current datasets have become high-dimensional and large-scale gradually. Therefore, this paper proposes a novel multi-view compressed subspace learning method via low-rank tensor constraint, which incorporates the clustering progress and multi-view learning into a unified framework. First, for each view, we take the partial samples to build a small-size dictionary, which can reduce the effect of both redundancy information and computation cost greatly. Then, to find the consistency and difference among views, we impose a low-rank tensor constraint on these representations and further design an auto-weighted mechanism to learn the optimal representation. Last, due to the non-square of the learned representation, the bipartite graph has been introduced, and under the structured constraint, the clustering results can be obtained directly from this graph without any post-processing. Extensive experiments on synthetic and real-world benchmark datasets demonstrate the efficacy and efficiency of our method, especially for the views with noise or outliers.