Multi-view clustering by joint spectral embedding and spectral rotation

Abstract

Discrete Spectral clustering is an effective tool for directly getting discrete labels. However, existing spectral clustering carry out spectral embedding and spectral rotation separately, which may limit their clustering performance. Seamlessly connecting above two processes is challenging. In this paper, we propose a new multi-view clustering framework, namely Multi-view Clustering by Joint Spectral Embedding and Spectral Rotation. In the framework, the differences of Laplacian matrices from different views are learned adaptively. Moreover, the real-valued cluster indicator matrix is approximated by continuous orthogonalization of the discrete clustering index matrix. By doing so, our method has better convergence, which is also strictly mathematically proven. Extensive experiments indicate that our method is superior to several state-of-the-art methods.