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Tensorized multi-view subspace representation learning

Tensorized multi-view subspace representation learning

Zhang, Changqing ORCID: 0000-0003-1410-6650, Fu, Huazhu, Wang, Jing, Li, Wen, Cao, Xiaochun and Hu, Qinghua (2020) Tensorized multi-view subspace representation learning. International Journal of Computer Vision, 128 (8-9). pp. 2344-2361. ISSN 0920-5691 (Print), 1573-1405 (Online) (doi:https://doi.org/10.1007/s11263-020-01307-0)

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Abstract

Self-representation based subspace learning has shown its effectiveness in many applications. In this paper, we promote the traditional subspace representation learning by simultaneously taking advantages of multiple views and prior constraint. Accordingly, we establish a novel algorithm termed as Tensorized Multi-view Subspace Representation Learning. To exploit different views, the subspace representation matrices of different views are regarded as a low-rank tensor, which effectively models the high-order correlations of multi-view data. To incorporate prior information, a constraint matrix is devised to guide the subspace representation learning within a unified framework. The subspace representation tensor equipped with a low-rank constraint models elegantly the complementary information among different views, reduces redundancy of subspace representations, and then improves the accuracy of subsequent tasks. We formulate the model with a tensor nuclear norm minimization problem constrained with ℓ2,1-norm and linear equalities. The minimization problem is efficiently solved by using an Augmented Lagrangian Alternating Direction Minimization method. Extensive experimental results on diverse multi-view datasets demonstrate the effectiveness of our algorithm.

Item Type: Article
Uncontrolled Keywords: Multi-view representation learning, Subspace clustering, Low-rank tensor, Constraint matrix
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 21 Feb 2021 01:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/29894

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