Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering

Wang, S., Chen, Y., Lin, Z., Cen, Y. and Cao, Q. (2023) Robustness meets low-rankness: unified entropy and tensor learning for multi-view subspace clustering. IEEE Transactions on Circuits and Systems for Video Technology, (doi: 10.1109/TCSVT.2023.3266801) (Early Online Publication)

Abstract

In this paper, we develop the weighted error entropy-regularized tensor learning method for multi-view subspace clustering (WETMSC), which integrates the noise disturbance removal and subspace structure discovery into one unified framework. Unlike most existing methods which focus only on the affinity matrix learning for the subspace discovery by different optimization models and simply assume that the noise is independent and identically distributed (i.i.d.), our WETMSC method adopts the weighted error entropy to characterize the underlying noise by assuming that noise is independent and piecewise identically distributed (i.p.i.d.). Meanwhile, WETMSC constructs the self-representation tensor by storing all self-representation matrices from the view dimension, preserving high-order correlation of views based on the tensor nuclear norm. To solve the proposed nonconvex optimization method, we design a half-quadratic (HQ) additive optimization technology and iteratively solve all subproblems under the alternating direction method of multipliers framework. Extensive comparison studies with state-of-the-art clustering methods on real-world datasets and synthetic noisy datasets demonstrate the ascendancy of the proposed WETMSC method.

• Item Type: Articles
• Additional Information: This work was supported in part by the National Key Research and Development Program of China under Grant 2021YFE0110500, in part by the National Natural Science Foundation of China under Grant 62062021 and 62106063, in part by the Guangdong Natural Science Foundation under Grant 2022A1515010819, and in part by the Shenzhen Science and Technology Program under Grant RCBS20210609103708013.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Cao, Dr Qi
• Authors: Wang, S., Chen, Y., Lin, Z., Cen, Y., and Cao, Q.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Circuits and Systems for Video Technology
• Publisher: IEEE
• ISSN: 1051-8215
• ISSN (Online): 1558-2205
• Published Online: 13 April 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Circuits and Systems for Video Technology 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy