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
Citation
ZHANG, Yue; HUANG, Qinjian; ZHANG, Bin; HE, Shengfeng; DAN, Tingting; PENG, Hong; and CAI, Hongmin.
Deep multiview clustering via iteratively self-supervised universal and specific space learning. (2021). IEEE Transactions on Cybernetics. 52, (11), 11734-11746.
Available at: https://ink.library.smu.edu.sg/sis_research/7851
Additional URL
https://doi.org/10.1109/TCYB.2021.3086153