Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/61656
Title: Projective robust nonnegative factorization
Authors: Lu, Y
Lai, Z
Xu, Y
You, J 
Li, X
Yuan, C
Keywords: Face recognition
Graph regularization
Nonnegative matrix factorization
Robust
Issue Date: 2016
Publisher: Elsevier
Source: Information sciences, 2016, v. 364-365, p. 16-32 How to cite?
Journal: Information sciences 
Abstract: Nonnegative matrix factorization (NMF) has been successfully used in many fields as a low-dimensional representation method. Projective nonnegative matrix factorization (PNMF) is a variant of NMF that was proposed to learn a subspace for feature extraction. However, both original NMF and PNMF are sensitive to noise and are unsuitable for feature extraction if data is grossly corrupted. In order to improve the robustness of NMF, a framework named Projective Robust Nonnegative Factorization (PRNF) is proposed in this paper for robust image feature extraction and classification. Since learned projections can weaken noise disturbances, PRNF is more suitable for classification and feature extraction. In order to preserve the geometrical structure of original data, PRNF introduces a graph regularization term which encodes geometrical structure. In the PRNF framework, three algorithms are proposed that add a sparsity constraint on the noise matrix based on L1/2 norm, L1 norm, and L2, 1 norm, respectively. Robustness and classification performance of the three proposed algorithms are verified with experiments on four face image databases and results are compared with state-of-the-art robust NMF-based algorithms. Experimental results demonstrate the robustness and effectiveness of the algorithms for image classification and feature extraction.
URI: http://hdl.handle.net/10397/61656
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2016.05.001
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
0
Last month
Citations as of Oct 19, 2017

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
Citations as of Oct 14, 2017

Page view(s)

49
Last Week
3
Last month
Checked on Oct 15, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.