Multi-view fuzzy clustering of deep random walk and sparse low-rank embedding

Abstract

Multi-view clustering aims to improve the learning performance by exploiting discriminative information from heterogeneous data sources. It has been capturing growing research attention due to its wide utilization in the areas of data mining and computer vision. However, the full use of complementarity and consistency from multi-view data is still a challenging task. Here, we propose a novel multi-view fuzzy clustering, which adopts the joint learning of deep random walk and sparse low-rank embedding. First, a deep random walk is employed to acquire a robust similarity matrix of data points and convert fuzzy membership matrix learning to adaptive graph learning. Second, the adaptive graph is restricted with sparse low-rank constraints, which ensures its strong discriminative ability and effective cluster assignments. Third, by exploiting the sparse low-rank property, the multi-view fuzzy clustering problem is formulated as optimizing a regularized graph adjacency matrix with distance metric learning and spectral norm minimization simultaneously. The distance metric learning is embedded to scatter all data points so that any two samples are projected onto a low-dimensional subspace with a large margin. Finally, an efficient algorithm is developed for the formulated problem and its convergence is also guaranteed. In order to demonstrate the superiority of the proposed method, extensive experiments are conducted by comparing state-of-the-arts on real-world databases.