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Adaptive multi-view semi-supervised nonnegative matrix factorization

Adaptive multi-view semi-supervised nonnegative matrix factorization

Wang, Jing, Wang, Xiao, Tian, Feng, Liu, Chang Hong, Yu, Hongchuan and Liu, Yanbei (2016) Adaptive multi-view semi-supervised nonnegative matrix factorization. In: ICONIP 2016: Neural Information Processing. Lecture Notes in Computer Science, 9948 . Springer, pp. 435-444. ISBN 978-3319466712 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-319-46672-9_49)

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Abstract

Multi-view clustering, which explores complementary information between multiple distinct feature sets, has received considerable attention. For accurate clustering, all data with the same label should be clustered together regardless of their multiple views. However, this is not guaranteed in existing approaches. To address this issue, we propose Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization (AMVNMF), which uses label information as hard constraints to ensure data with same label are clustered together, so that the discriminating power of new representations are enhanced. Besides, AMVNMF provides a viable solution to learn the weight of each view adaptively with only a single parameter. Using L2,1 -norm, AMVNMF is also robust to noises and outliers. We further develop an efficient iterative algorithm for solving the optimization problem. Experiments carried out on five well-known datasets have demonstrated the effectiveness of AMVNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.

Item Type: Conference Proceedings
Title of Proceedings: ICONIP 2016: Neural Information Processing
Uncontrolled Keywords: multi-view learning, semi-supervised learning, nonnegative matrix factorization
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: 26 Apr 2021 08:32
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/30505

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