Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering

TL;DR

This paper introduces a novel multi-view subspace clustering method that assigns feature weights and captures local structures, improving clustering accuracy by leveraging view-specific information and ensuring view consistency.

Contribution

It proposes a new multi-view clustering approach that considers local structures and feature weighting, addressing noise and redundancy issues in multi-view data.

Findings

Achieves state-of-the-art clustering performance on benchmark datasets.

Effectively exploits local structures and feature importance across views.

Demonstrates robustness against noisy and redundant data.

Abstract

Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance. It remains challenging to effectively exploit complementary information across multiple views since the original data often contain noise and are highly redundant. Moreover, most existing multi-view clustering methods only aim to explore the consistency of all views while ignoring the local structure of each view. However, it is necessary to take the local structure of each view into consideration, because different views would present different geometric structures while admitting the same cluster structure. To address the above issues, we propose a novel multi-view subspace clustering method via simultaneously assigning weights for different features and capturing local information of data in view-specific self-representation feature spaces. Especially, a common cluster structure regularization is adopted to guarantee consistency among different views. An efficient algorithm based on an augmented Lagrangian multiplier is also developed to solve the associated optimization problem. Experiments conducted on several benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance. We provide the Matlab code on https://github.com/Ekin102003/JFLMSC.