Guided co-training for multi-view spectral clustering

Chung-Kuei Lee, Tyng-Luh Liu

Abstract: We address the problem of how to design a more effective co-training scheme to tackle the multi-view spectral clustering. The conventional co-training procedure treats information from all views equally and often converges to a compromised consensus view that does not fully utilize the multiview information. We instead propose to learn an augmented view and construct its corresponding affinity matrix from a spectral decomposition of an information-rich matrix formed by the eigenvectors of the Laplacian matrices from all views. As the augmented view is expected to be more favorable for carrying out spectral clustering, we design a new pairwise co-training procedure to guide the improvements of the given multiple views separately and iteratively. Our experimental results on three popular benchmark datasets support that the convergent augmented view by the guided co-training process is useful to multi-view spectral clustering, and can yield state-of-the-art performance.