Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation

Li, Ping, Bu, Jiajun, Yang, Yi, Ji, Rongrong, Chen, Chun and Cai, Deng (2014) Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation. Expert Systems with Applications, 41 4 PART 1: 1283-1293. doi:10.1016/j.eswa.2013.08.026

Author Li, Ping
Bu, Jiajun
Yang, Yi
Ji, Rongrong
Chen, Chun
Cai, Deng
Title Discriminative Orthogonal Nonnegative matrix factorization with flexibility for data representation
Journal name Expert Systems with Applications   Check publisher's open access policy
ISSN 0957-4174
Publication date 2014-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.eswa.2013.08.026
Open Access Status Not yet assessed
Volume 41
Issue 4 PART 1
Start page 1283
End page 1293
Total pages 11
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Language eng
Subject 2200 Engineering
1706 Computer Science Applications
1702 Artificial Intelligence
Abstract Learning an informative data representation is of vital importance in multidisciplinary applications, e.g., face analysis, document clustering and collaborative filtering. As a very useful tool, Nonnegative matrix factorization (NMF) is often employed to learn a well-structured data representation. While the geometrical structure of the data has been studied in some previous NMF variants, the existing works typically neglect the discriminant information revealed by the between-class scatter and the total scatter of the data. To address this issue, we present a novel approach named Discriminative Orthogonal Nonnegative matrix factorization (DON), which preserves both the local manifold structure and the global discriminant information simultaneously through manifold discriminant learning. In particular, to learn the discriminant structure for the data representation, we introduce the scaled indicator matrix, which naturally satisfies the orthogonality condition. Thus, we impose the orthogonality constraints on the objective function. However, too heavy constraints will lead to a very sparse data representation that is unexpected in reality. So we further make this orthogonality flexible. In addition, we provide the optimization framework with the convergence proof of the updating rules. Extensive comparisons over several state-of-the-art approaches demonstrate the efficacy of the proposed method.
Keyword Data representation
Flexible orthogonality
Manifold discriminant learning
Nonnegative matrix factorization
Q-Index Code C1
Q-Index Status Confirmed Code
Grant ID 91120302
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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