Multiview Subspace Clustering Using Low-Rank Representation

Chen, Jie, Yang, Shengxiang, Mao, Hua and Fahy, Conor (2021) Multiview Subspace Clustering Using Low-Rank Representation. IEEE Transactions on Cybernetics. ISSN 2168-2267 (In Press)

[img]
Preview
Text
IEEETCYB21.pdf - Accepted Version

Download (10MB) | Preview
Official URL: https://doi.org/10.1109/TCYB.2021.3087114

Abstract

Multiview subspace clustering is one of the most widely used methods for exploiting the internal structures of multiview data. Most previous studies have performed the task of learning multiview representations by individually constructing an affinity matrix for each view without simultaneously exploiting the intrinsic characteristics of multiview data. In this article, we propose a multiview low-rank representation (MLRR) method to comprehensively discover the correlation of multiview data for multiview subspace clustering. MLRR considers symmetric low-rank representations (LRRs) to be an approximately linear spatial transformation under the new base, that is, the multiview data themselves, to fully exploit the angular information of the principal directions of LRRs, which is adopted to construct an affinity matrix for multiview subspace clustering, under a symmetric condition. MLRR takes full advantage of LRR techniques and a diversity regularization term to exploit the diversity and consistency of multiple views, respectively, and this method simultaneously imposes a symmetry constraint on LRRs. Hence, the angular information of the principal directions of rows is consistent with that of columns in symmetric LRRs. The MLRR model can be efficiently calculated by solving a convex optimization problem. Moreover, we present an intuitive fusion strategy for symmetric LRRs from the perspective of spectral clustering to obtain a compact representation, which can be shared by multiple views and comprehensively represents the intrinsic features of multiview data. Finally, the experimental results based on benchmark datasets demonstrate the effectiveness and robustness of MLRR compared with several state-of-the-art multiview subspace clustering algorithms.

Item Type: Article
Additional Information: Funding information: National Key Research and Development Program of China (Grant Number: 2018YFC0831900) 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61303015 and 61673331) AI in Law Advanced Deployed Discipline of Sichuan University China
Uncontrolled Keywords: Adaptation models, Clustering algorithms, Data models, Feature extraction, low-rank representation (LRR), Multiview data, Probabilistic logic, spectral clustering, subspace clustering, Symmetric matrices, Task analysis
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 03 Mar 2022 14:51
Last Modified: 03 Mar 2022 15:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48604

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics