Diversity and consistency learning guided spectral embedding for multi-view clustering

Multi-view clustering aims to group data points into their classes. Exploiting the complementary information underlying multiple views to benefit the clustering performance is one of the topics of multi-view clustering. Most of existing multi-view clustering methods only constrain diversity and consistency in the data space, but not consider the diversity and consistency in the learned label space. However, It is natural to take the impacts of diversity in the learned label matrix into consideration, because different view would generate different clustering label matrix, in which some labels are consistent and some are diverse. To overcome this issue, we propose a novel multi-view clustering method (DCMSC) by constraining diversity and consistency in both the learned clustering label matrix and data space. Specifically, in the learned label space, we relax the learned common label matrix into consistent part and diverse part. Meanwhile, by applying an introduced row-aware diversity representation and l(2,1)-norm to constrain diverse part, wrong-labels and the influences of noises on the consistent part are reduced. In the data space, we weight each view by using a self-weight strategy. Furthermore, we conduct clustering in spectral embedded spaces instead of original data spaces, which suppresses the effect of noises and decreases redundant information. An augmented Lagrangian multiplier with alternating direction minimization (ALM-ADM) based optimization solution can guarantee the convergence of our method. Extensive experimental results on both synthetic datasets and real-world datasets demonstrate the effectiveness of our method. (C) 2019 Elsevier B.V. All rights reserved.