Simultaneously learning feature-wise weights and local structures for multi-view subspace clustering

Abstract

Multi-view clustering integrates multiple feature sets, which usually have a complementary relationship and can reveal distinct insights of data from different angles, to improve clustering performance. It remains challenging to productively utilize complementary information across multiple views since there is always noise in real data, and their features are highly redundant. Moreover, most existing multi-view clustering approaches only aimed at exploring the consistency of all views, but overlooked the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because individual views generally present different geometric structures while admitting the same cluster structure. To ease the above issues, in this paper, a novel multi-view subspace clustering method is established by concurrently assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. In particular, a common clustering assignment regularization is adopted to explore the consistency among multiple views. An alternating iteration algorithm based on the augmented Lagrangian multiplier is also developed for optimizing the associated objective. Experiments conducted on diverse multi-view datasets manifest that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.