Multi-view Clustering via Diversity Induction and Orthogonal Non-negative Graph Reconstruction

Abstract

Multi-view clustering algorithm based on graph learning has been widely concerned in recent years because of its simplicity and high efficiency. Most of multi-view clustering algorithms only consider the consistent part of each view and ignore the diversity between different views. Moreover, most of the methods learn the similarity map directly from the original data points, which cannot show the clear cluster structure and accurately extract the underlying information. To solve the above problems, a multi-view clustering algorithm via diversity induction and orthogonal non-negative graph reconstruction (DOMVC) is proposed. Firstly, the consistency and diversity of multiple views are fully utilized within a unified framework, and the consistent part is fused into the target graph with adaptive weights to produce a more reasonable clustering structure. Then, spectral clustering and non-negative matrix decomposition are integrated to obtain the cluster target graph with clearer structure. Finally, a factor matrix of non-negative matrix decomposition is constrained to an orthogonal indicator matrix, and the clustering results are obtained directly. The algorithm automatically assigns appropriate view weights according to the clustering ability of each view. In addition, to solve the optimization problem jointly, an alternate iteration strategy is used to optimize the objective function of the clustering algorithm. Experimental results show that the proposed algorithm can effectively improve the clustering accuracy. The accuracy on the HW2, 100leaves and Mfeat datasets reaches 99.21%, 89.56% and 87.85%, respectively, with an accuracy improvement of 0.81 percentage points, 5.12 percentage points and 3.75 percentage points compared with the suboptimal model. Theoretical analysis and experimental research demonstrate the effectiveness and excellent performance of the proposed algorithm.

Keywords

• multi-view clustering; diversity induction; graph reconstruction; spectral embedding; non-negative matrix factorization