An attention-based framework for multi-view clustering on Grassmann manifold. (August 2022)

• Record Type: Journal Article
• Authors: Wu, Danyang; Dong, Xia; Nie, Feiping; Wang, Rong; Li, Xuelong
• Is Part Of: Pattern recognition. Volume 128(2022)
• Publication Date: 2022-08
• Subjects: Multi-view clustering -- Grassmann manifold -- Principle angles -- Attentive weighted-learning scheme; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2022.108610
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: This work can effectively mine the manifold structures of subspaces. This work can adaptively capture the differences among views. This work can generate clustering results without randomness. This work is the first multi-view clustering framework built on Grassmann manifold. This work is extensible and can generate many models. Abstract: The key problem of multi-view clustering is to handle the inconsistency among multiple views. This article proposes an attention-based framework for multi-view clustering on Grassmann manifold (AMCGM). To be specific, the proposed AMCGM framework aims to learn a representative element on Grassmann manifold with the following four highlights: 1) AMCGM framework performs an attention-based weighted-learning scheme to capture the difference of views; 2) The clustering results can be directly generated by the structured graph learned via AMCGM, avoiding the randomness caused by traditional label-generation procedures, such as K -means clustering; 3) AMCGM has high extensibility since it can generate many multi-view clustering models on Grassmann manifold; 4) On Grassmann manifold, the relationship between the projection metric (PM)-based multi-view clustering model and squared projection metric (SPM)-based model is studied. Based on AMCGM framework, we propose some generated models and provide some useful conclusions. Moreover, to solve the optimization problems involved in the proposed AMCGM framework and generated models, we propose an efficiently iterative algorithm and provide rigorous convergence analysis. Extensive experimental results demonstrate the superb performance of our framework.