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Title: Dual semi-supervised convex nonnegative matrix factorization for data representation
Authors: Peng, Siyuan
Yang, Zhijing
Ling, Bingo Wing-Kuen
Chen, Badong
Lin, Zhiping
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Peng, S., Yang, Z., Ling, B. W., Chen, B. & Lin, Z. (2022). Dual semi-supervised convex nonnegative matrix factorization for data representation. Information Sciences, 585, 571-593.
Journal: Information Sciences
Abstract: Semi-supervised nonnegative matrix factorization (NMF) has received considerable attention in machine learning and data mining. A new semi-supervised NMF method, called dual semi-supervised convex nonnegative matrix factorization (DCNMF), is proposed in this paper for fully using the limited label information. Specifically, DCNMF simultaneously incorporates the pointwise and pairwise constraints of labeled samples as dual supervisory information into convex NMF, which results in a better low-dimensional data representation. Moreover, DCNMF imposes the nonnegative constraint only on the coefficient matrix but not on the base matrix. Consequently, DCNMF can process mixed-sign data, and hence enlarge the range of applications. We derive an efficient alternating iterative algorithm for DCNMF to solve the optimization, and analyze the proposed DCNMF method in terms of the convergence and computational complexity. We also discuss the relationships between DCNMF and several typical NMF based methods. Experimental results illustrate that DCNMF outperforms the related state-of-the-art NMF methods on nonnegative and mixed-sign datasets for clustering applications.
ISSN: 0020-0255
DOI: 10.1016/j.ins.2021.11.045
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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