Scalable One-Pass Incomplete Multi-View Clustering by Aligning Anchors

Yalan Qin, Guorui Feng, Xinpeng Zhang

Abstract: Multi-view clustering has gained increasing attention by utilizing the complementary and consensus information across views. To alleviate the computation cost for the existing multi-view clustering approaches on datasets with large scales, studies based on anchor have been presented. Although extensively adopted in the real scenarios, most of these works ignore to learn an integral subspace revealing the cluster structure with anchors from different views being aligned, where the centroid and cluster assignment matrix can be directly achieved based on the integral subspace. Moreover, these works neglect to perform the alignment among anchors and integral subspace learning in a unified model on the incomplete multi-view dataset. Then the mutual improvements among aligning anchors and learning integral subspace are not guaranteed in optimizing the objective function, which inevitably limit the representation ability of the model and result in the suboptimal clustering performance. In this paper, we propose a novel anchor learning method for incomplete multi-view dataset termed Scalable One-pass incomplete Multi-view clustEring by Aligning anchorS (SOME-AS). Specifically, we capture the complementary information among multiple views by building the anchor graph for each view on the incomplete dataset. The integral subspace reflecting the cluster structure is learned with the alignment among anchors from different views being considered. We build the cluster assignment and centroid representation with orthogonal constraint to approximate the integral subspace. Then the subspace itself and the partition are simultaneously taken into account in this manner. Besides, the mutual improvements among aligning anchors and learning integral subspace are able to be ensured. Experiments on several incomplete multi-view datasets validate the efficiency and effectiveness of SOME-AS.