Tensor-Based Incomplete Multi-View Clustering With Low-Rank Data Reconstruction and Consistency Guidance
Wenyu Hao, Shanmin Pang, Xiuxiu Bai, Jianru Xue
Abstract
We propose a new approach, called Tensor-based Incomplete Multi-view Clustering with Low-rank data Reconstruction and Consistency guidance (TIMC-RC), to perform clustering on multi-view data with missing views. Existing methods usually leverage original incomplete data to explore the partial correlations among multiple views, and do not make sufficient use of both consistent and complementary information across views. To explore the full information of missing and available views, TIMC-RC introduces low-rank data reconstruction and consistency view establishment. Specifically, 1) it adopts a low-rank constraint to reconstruct data representations so as to reduce the negative effect of missing data and obtain more reasonable data representations. 2) It builds a new consistency view by self-representation matrices and therefore explores the consistent correlation of different views. 3) It formalizes view-specific self-representation matrices and the consistent matrix as a tensor and utilizes the tensor singular value decomposition-based nuclear norm to enhance the consistency and complementarity of multi-view representations. Experiments conducted on eight benchmarks verify the effectiveness and advancement of the proposed TIMC-RC.