Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
This work addresses the problem of improving clustering accuracy in multi-view data analysis, which is incremental as it builds on existing methods by incorporating local structure and feature weighting.
The authors tackled the challenge of effectively exploiting complementary information in multi-view clustering by proposing a method that simultaneously assigns feature weights and captures local structure in view-specific self-representation spaces, achieving state-of-the-art performance on benchmark datasets.
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.