Similarity-Induced Weighted Consensus Laplacian Matrix Learning for Multiview Clustering

Hui Liu, Xiao Yu, Yuxiu Lin, Xuemeng Song, Liqiang Nie

Abstract: Multiview spectral clustering, which stands out with its remarkable clustering performance, has drawn increasing research attention. Its core is properly weighing the contribution of different views and comprehensively utilizing multiview information in the clustering process. Although existing methods, like exponential decay and root loss, have achieved significant progress, they still have limitations in their weighting scheme, application generalization, and model efficiency. To handle these limitations, we propose a novel Similarity-Induced Weighted Consensus Laplacian matrix learning method for multiview clustering (MC), named SIWCL. This method has two distinctive features: 1) instead of conventional Laplacian matrix learning, SIWCL resorts to consensus Laplacian matrix learning as the MC framework for effectively exploiting complementary information from multiple views and 2) we argue that there could be outlier views that exhibit an uneven similarity distribution with other views, and equally treating them with other views can hurt model performance. Therefore, beyond consensus Laplacian matrix learning, SIWCL introduces a novel weighting strategy that adaptively assigns weights to views according to their consistency with other views based on the multiview similarity matrices. Notably, this novel method has only one hyperparameter and closed-form solutions, greatly improving the efficiency and generalization. Experiments show that the weights obtained by the proposed weighting strategy are correlated to the quality of the clustering structure. The comparisons between the proposed method and other state-of-the-art baseline methods over eight datasets demonstrate the robustness and superior clustering performance of SIWCL.