Adaptive Multi-view Semi-supervised Nonnegative Matrix Factorization

This source preferred by Feng Tian

Authors: Wang, J., Tian, F., Liu, C.H. and Yu, H.C.

http://eprints.bournemouth.ac.uk/24965/

Start date: 16 October 2016

This data was imported from Scopus:

Authors: Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H. and Liu, Y.

http://eprints.bournemouth.ac.uk/24965/

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

© Springer International Publishing AG 2016. 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.

This data was imported from Web of Science (Lite):

Authors: Wang, J., Wang, X., Tian, F., Liu, C.H., Yu, H. and Liu, Y.

http://eprints.bournemouth.ac.uk/24965/

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

The data on this page was last updated at 04:52 on November 15, 2018.