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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 5/2015

01-10-2015 | Original Article

Nonnegative matrix factorization with manifold regularization and maximum discriminant information

Authors: Wenjun Hu, Kup-Sze Choi, Jianwen Tao, Yunliang Jiang, Shitong Wang

Published in: International Journal of Machine Learning and Cybernetics | Issue 5/2015

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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.
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Footnotes
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Metadata
Title
Nonnegative matrix factorization with manifold regularization and maximum discriminant information
Authors
Wenjun Hu
Kup-Sze Choi
Jianwen Tao
Yunliang Jiang
Shitong Wang
Publication date
01-10-2015
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 5/2015
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-015-0396-8

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