Deep multiview clustering via iteratively self-supervised universal and specific space learning

Publication Type

Journal Article

Publication Date

6-2021

Abstract

Multiview clustering seeks to partition objects via leveraging cross-view relations to provide a comprehensive description of the same objects. Most existing methods assume that different views are linear transformable or merely sampling from a common latent space. Such rigid assumptions betray reality, thus leading to unsatisfactory performance. To tackle the issue, we propose to learn both common and specific sampling spaces for each view to fully exploit their collaborative representations. The common space corresponds to the universal self-representation basis for all views, while the specific spaces are the view-specific basis accordingly. An iterative self-supervision scheme is conducted to strengthen the learned affinity matrix. The clustering is modeled by a convex optimization. We first solve its linear formulation by the popular scheme. Then, we employ the deep autoencoder structure to exploit its deep nonlinear formulation. The extensive experimental results on six real-world datasets demonstrate that the proposed model achieves uniform superiority over the benchmark methods.

Keywords

Feature extraction, Tensors, Kernel, Correlation, Task analysis, Numerical models, Training, Deep autoencoder, multiview clustering, self-supervised, universal and specific space learning

Discipline

Information Security

Research Areas

Information Systems and Management

Publication

IEEE Transactions on Cybernetics

Volume

52

Issue

11

First Page

11734

Last Page

11746

ISSN

2168-2267

Identifier

10.1109/TCYB.2021.3086153

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TCYB.2021.3086153

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