Please use this identifier to cite or link to this item:
Title: Nonnegative matrix factorization with manifold regularization and maximum discriminant information
Authors: Hu, W
Choi, KS 
Tao, J
Jiang, Y
Wang, S
Keywords: Clustering
Manifold regularization
Maximum information
Nonnegative matrix factorization
Issue Date: 2015
Publisher: Springer
Source: International journal of machine learning and cybernetics, 2015, v. 6, no. 5, p. 837-846 How to cite?
Journal: International journal of machine learning and cybernetics 
Abstract: Nonnegative matrix factorization (NMF) has been successfully used in different applications including computer vision, pattern recognition and text mining. NMF aims to decompose a data matrix into the product of two matrices (respectively denoted as the basis vectors and the encoding vectors), whose entries are constrained to be nonnegative. Unlike the ordinary NMF, we propose a novel NMF, denoted as MMNMF, which considers both geometrical information and discriminative information hidden in the data. The geometrical information is discovered by minimizing the distance among the encoding vectors, while the discriminative information is uncovered by maximizing the distance among base vectors. Clustering experiments are performed on the real-world data sets of faces, images, and documents to demonstrate the effectiveness of the proposed algorithm.
ISSN: 1868-8071 (print)
1868-808X (online)
DOI: 10.1007/s13042-015-0396-8
Appears in Collections:Journal/Magazine Article

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

Page view(s)

Last Week
Last month
Citations as of Nov 19, 2018

Google ScholarTM



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