Robust Semi-supervised Nonnegative Matrix Factorization

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

http://eprints.bournemouth.ac.uk/22724/

Start date: 12 July 2015

This data was imported from Scopus:

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

http://eprints.bournemouth.ac.uk/22724/

Journal: Proceedings of the International Joint Conference on Neural Networks

Volume: 2015-September

ISBN: 9781479919604

DOI: 10.1109/IJCNN.2015.7280422

© 2015 IEEE. Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a range of applications such as data clustering, pattern recognition and computer vision. Real-world data are often sparse and noisy which may reduce the accuracy of representations. And a small portion of data may have prior label information, which, if utilized, can improve the discriminability of representations. In this paper, we propose a robust semi-supervised nonnegative matrix factorization (RSSN-MF) approach which takes all factors above into consideration. RSSNMF incorporates the label information as an additional constraint to guarantee that the data with the same label have the same representation. It addresses the sparsity of data and accommodates noises and outliers consistently via L 2,1 -norm. An iterative updating optimization scheme is derived to solve RSSNMF's objective function. We have proven the convergence of this optimization scheme by utilizing auxiliary function method and the correctness based on the Karush-Kohn-Tucker condition of optimization theory. Experiments carried on well-known data sets demonstrate the effectiveness of RSSNMF in comparison to other existing state-of-the-art approaches in terms of accuracy and normalized mutual information.

This source preferred by Feng Tian and Changhong Liu

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

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

http://eprints.bournemouth.ac.uk/22724/

Journal: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

ISSN: 2161-4393

The data on this page was last updated at 04:45 on January 17, 2018.