Structured general and specific multi-view subspace clustering. (September 2019)

• Record Type: Journal Article
• Authors: Zhu, Wencheng; Lu, Jiwen; Zhou, Jie
• Is Part Of: Pattern recognition. Volume 93(2019:Sep.)
• Page Start: 392
• Page End: 403
• Publication Date: 2019-09
• Subjects: Subspace clustering -- Multi-view learning -- Structure consistence -- Diversity; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2019.05.005
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: We propose a structured general and specific multi-view subspace clustering method for image clustering. The structural general representation matrix keeps the similarity relationship of data and the specific representation matrices exploit the diversity between different matrices. We present an effective optimization algorithm to solve the proposed objective function. Compared with most state-of-the-arts, experimental results demonstrate that our proposed methods obtain superior performances on four benchmark datasets. Abstract: In this paper, we propose a structured general and specific multi-view subspace clustering method for image clustering. Unlike most existing multi-view subspace clustering methods which harness the shared cluster structure to preserve the consistence between different views or utilize the diversity regularization to exploit the complementary information from different views, our method learns the structured general and specific representation matrices to obtain the common and specific characteristics of different views with structure consistence and diversity regularization. The general representation matrix guarantees the consistence between different views and the specific representation matrices indicate the diversity among different views. Hence, our method can well exploit the common structure and diversity information of multi-view data. Specifically, the proposed framework can be applied into many existing multi-view subspace clustering methods. Moreover, we develop an efficient and effective optimization approach to solve the objective function of which the time and convergence analyses are also provided. Experimental results on four benchmark datasets are presented to show the effectiveness of proposed method.