Adaptive multi-view semi-supervised nonnegative matrix factorization
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H. and Liu, Y.
Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 9948 LNCS
Pages: 435-444
eISSN: 1611-3349
ISBN: 9783319466712
ISSN: 0302-9743
DOI: 10.1007/978-3-319-46672-9_49
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.
https://eprints.bournemouth.ac.uk/24965/
Source: Scopus
Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H. and Liu, Y.
Journal: NEURAL INFORMATION PROCESSING, ICONIP 2016, PT II
Volume: 9948
Pages: 435-444
eISSN: 1611-3349
ISBN: 978-3-319-46671-2
ISSN: 0302-9743
DOI: 10.1007/978-3-319-46672-9_49
https://eprints.bournemouth.ac.uk/24965/
Source: Web of Science (Lite)
Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Liu, C.H. and Yu, H.C.
Conference: 23rd International Conference on Neural Information Processing
Dates: 16-21 October 2016
https://eprints.bournemouth.ac.uk/24965/
Source: Manual
Adaptive Multi-View Semi-Supervised Nonnegative Matrix Factorization
Authors: Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H. and Liu, Y.
Editors: Akira, H., Seiichi, O., Doya, K., Kazushi, I., Minho, L. and Derong, L.
Conference: 23rd International Conference on Neural Information Processing
Pages: 435-444
Publisher: Springer
ISBN: 978-3-319-46672-9
ISSN: 0302-9743
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.
https://eprints.bournemouth.ac.uk/24965/
http://link.springer.com/chapter/10.1007%2F978-3-319-46672-9_49
Source: BURO EPrints