Robust semi-supervised nonnegative matrix factorization
Authors: Wang, J., Tian, F., Liu, C.H. and Wang, X.
Journal: Proceedings of the International Joint Conference on Neural Networks
Volume: 2015-September
ISBN: 9781479919604
DOI: 10.1109/IJCNN.2015.7280422
Abstract: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 L2,1-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.
https://eprints.bournemouth.ac.uk/22724/
Source: Scopus
Robust Semi-supervised Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Liu, C.H. and Wang, X.
Journal: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
ISSN: 2161-4393
https://eprints.bournemouth.ac.uk/22724/
Source: Web of Science (Lite)
Robust Semi-supervised Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Liu, C.H. and Wang, X.
Conference: International Joint Conference on Neural Network
Dates: 12-17 July 2015
https://eprints.bournemouth.ac.uk/22724/
Source: Manual
Robust Semi-supervised Nonnegative Matrix Factorization
Authors: Wang, J., Tian, F., Liu, C. and Wang, X.
Conference: International Joint Conference on Neural Networks
Abstract:Nonnegative matrix factorization (NMF), which aims at finding parts-based representations of nonnegative data, has been widely applied to a wide 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 part of data may have prior label information, which, if utilized, may improve the discriminability of representations. In this paper, we propose a robust semi-supervised nonnegative matrix factorization (RSSNMF) approach which takes all factors 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 L2;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
https://eprints.bournemouth.ac.uk/22724/
Source: BURO EPrints