Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering

Wang, S., Lin, Z., Cao, Q. , Cen, Y. and Chen, Y. (2023) Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering. IEEE Transactions on Image Processing, (doi: 10.1109/TIP.2023.3293764) (Early Online Publication)

Abstract

Multi-view subspace clustering aims to integrate the complementary information contained in different views to facilitate data representation. Currently, low-rank representation (LRR) serves as a benchmark method. However, we observe that these LRR-based methods would suffer from two issues: limited clustering performance and high computational cost since (1) they usually adopt the nuclear norm with biased estimation to explore the low-rank structures; (2) the singular value decomposition of large-scale matrices is inevitably involved. Moreover, LRR may not achieve low-rank properties in both intra-views and interviews simultaneously. To address the above issues, this paper proposes the Bi-nuclear tensor Schatten-p norm minimization for multi-view subspace clustering (BTMSC). Specifically, BTMSC constructs a third-order tensor from the view dimension to explore the high-order correlation and the subspace structures of multi-view features. The Bi-Nuclear Quasi-Norm (BiN) factorization form of the Schatten-p norm is utilized to factorize the third-order tensor as the product of two small-scale thirdorder tensors, which not only captures the low-rank property of the third-order tensor but also improves the computational efficiency. Finally, an efficient alternating optimization algorithm is designed to solve the BTMSC model. Extensive experiments with ten datasets of texts and images illustrate the performance superiority of the proposed BTMSC method over state-of-the-art methods.

• 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., Lin, Z., Cao, Q., Cen, Y., and Chen, Y.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: IEEE Transactions on Image Processing
• Publisher: IEEE
• ISSN: 1057-7149
• ISSN (Online): 1941-0042
• Published Online: 13 July 2023
• Copyright Holders: Copyright © 2023 IEEE
• First Published: First published in IEEE Transactions on Image Processing 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy