Orthogonal Basis-Based Multiview Transfer Spectral Clustering

The consistency of multiview data is important for multiview clustering.To achieve multiview data with better consistency, this paper proposes a new multiview clustering algorithm, OMTSC.The OMTSC algorithm simultaneously learns the cluster assignment matrix and feature embedding of each view.Each cluster assignment matrix can be decomposed into shared orthogonal basis-cluster coding matrices.An orthogonal basis matrix can capture and store consistent multiview data and form latent cluster centers.A weighted multiview cluster coding matrix can balance the quality differences of different views effectively.Meanwhile, bipartite graph co-clustering is introduced to realize knowledge transfer, which involves clustering coding, feature embedding, and the orthogonal basis.This improves the multiview data consistency and diversity learning, as well as allows the OMTSC algorithm to leverage the diversity of feature embedding for maximizing multiview consistency and learning the optimal latent cluster centers, thus further improving the performance of multiview clustering.In addition, feature embedding based on group sparse constraints is robust to noise in view data.Experimental results on WikipediaArticles, COIL20, and ORL datasets show that the OMTSC algorithm is superior to SC-Best, Co-Reg, and advanced multiview clustering algorithms, and that it yields the highest score in all three evaluation indexes, i.e., the ACC, NMI, and ARI on COIL20 and ORL datasets, the NMI evaluation index for the OMTSC algorithm exceeds 0.9.