Multiview Clustering: A Scalable and Parameter-Free Bipartite Graph Fusion Method

• Published in: IEEE transactions on pattern analysis and machine intelligence Vol. 44; no. 1; pp. 330 - 344
• Main Authors: Li, Xuelong, Zhang, Han, Wang, Rong, Nie, Feiping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bipartite graph; Clustering; Clustering algorithms; Computer science; connectivity constraint; Data models; Electronic mail; graph fusion; Graph theory; Graphs; initialization-independent; Laplace equations; Mathematical models; Multiview clustering; Optical imaging; Parameters; Regularization; scalable and parameter-free
• ISSN: 0162-8828; 1939-3539; 2160-9292
• DOI: 10.1109/TPAMI.2020.3011148

Multiview clustering partitions data into different groups according to their heterogeneous features. Most existing methods degenerate the applicability of models due to their intractable hyper-parameters triggered by various regularization terms. Moreover, traditional spectral based methods always encounter the expensive time overheads and fail in exploring the explicit clusters from graphs. In this paper, we present a scalable and parameter-free graph fusion framework for multiview clustering, seeking for a joint graph compatible across multiple views in a self-supervised weighting manner. Our formulation coalesces multiple view-wise graphs straightforward and learns the weights as well as the joint graph interactively, which could actively release the model from any weight-related hyper-parameters. Meanwhile, we manipulate the joint graph by a connectivity constraint such that the connected components indicate clusters directly. The designed algorithm is initialization-independent and time-economical which obtains the stable performance and scales well with the data size. Substantial experiments on toy data as well as real datasets are conducted that verify the superiority of the proposed method compared to the state-of-the-arts over the clustering performance and time expenditure.