Multigraph Random Walk for Joint Learning of Multiview Clustering and Semisupervised Classification

Recent researches on multiview learning have received widespread attention due to the increasing generalization of multiview data. As an effective probabilistic model, random walk has also shown encouraging performance in various fields. To further exploit the potential of utilizing random walk schemes to address multiview learning problems, this article proposes a simple yet efficient multigraph random walk scheme for both multiview clustering and semisupervised classification tasks. The proposed model integrates random walk with multiview learning, and recursively learns a globally stable probability distribution matrix from multiple views, on the basis of which the label indicator is obtained in the scene of clustering or semisupervised classification. Furthermore, an adaptive weight vector is learned to incorporate the diversity and complementarity of multiview data. Besides, the relationships between the proposed scheme and spectral clustering, neighborhood embedding and manifold embedding are analyzed theoretically. Finally, comprehensive comparative experiments are conducted with several state-of-the-art multiview clustering and semisupervised classification methods on eight real-world datasets. The experimental results demonstrate the superiority of the proposed method in terms of both clustering and classification performance.

Automated summary

Recent research has proposed a multigraph random walk scheme for addressing multiview clustering and semisupervised classification tasks, which integrates random walk with multiview learning. The scheme recursively learns a globally stable probability distribution matrix from multiple views to obtain label indicators for clustering or semisupervised classification. An adaptive weight vector is also learned to incorporate the diversity and complementarity of multiview data. Comprehensive comparative experiments demonstrate the superiority of the proposed method in terms of both clustering and classification performance.