Skip navigation

Robust semi-supervised nonnegative matrix factorization

Robust semi-supervised nonnegative matrix factorization

Wang, Jing, Tian, Feng, Liu, Chang Hong and Wang, Xiao (2015) Robust semi-supervised nonnegative matrix factorization. In: 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1-8. ISBN 978-1479919604 ISSN 2161-4393 (Print), 2161-4407 (Online) (doi:https://doi.org/10.1109/IJCNN.2015.7280422)

[img]
Preview
PDF (Author's Accepted Manuscript)
30506 WANG_Robust_Semi-supervised_Nonnegative_Matrix_Factorization_(AAM)_2015.pdf - Accepted Version

Download (129kB) | Preview

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 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.

Item Type: Conference Proceedings
Title of Proceedings: 2015 International Joint Conference on Neural Networks (IJCNN)
Uncontrolled Keywords: nonnegative matrix factorization, Semi-supervised learning, clustering
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: 23 Apr 2021 14:04
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/30506

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics