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Data representation using robust nonnegative matrix factorization for edge computing.

Authors :
Yang Q
Chen J
Al-Nabhan N
Source :
Mathematical biosciences and engineering : MBE [Math Biosci Eng] 2022 Jan; Vol. 19 (2), pp. 2147-2178. Date of Electronic Publication: 2021 Dec 28.
Publication Year :
2022

Abstract

As a popular data representation technique, Nonnegative matrix factorization (NMF) has been widely applied in edge computing, information retrieval and pattern recognition. Although it can learn parts-based data representations, existing NMF-based algorithms fail to integrate local and global structures of data to steer matrix factorization. Meanwhile, semi-supervised ones ignore the important role of instances from different classes in learning the representation. To solve such an issue, we propose a novel semi-supervised NMF approach via joint graph regularization and constraint propagation for edge computing, called robust constrained nonnegative matrix factorization (RCNMF), which learns robust discriminative representations by leveraging the power of both L2, 1-norm NMF and constraint propagation. Specifically, RCNMF explicitly exploits global and local structures of data to make latent representations of instances involved by the same class closer and those of instances involved by different classes farther. Furthermore, RCNMF introduces the L2, 1-norm cost function for addressing the problems of noise and outliers. Moreover, L2, 1-norm constraints on the factorial matrix are used to ensure the new representation sparse in rows. Finally, we exploit an optimization algorithm to solve the proposed framework. The convergence of such an optimization algorithm has been proven theoretically and empirically. Empirical experiments show that the proposed RCNMF is superior to other state-of-the-art algorithms.

Details

Language :
English
ISSN :
1551-0018
Volume :
19
Issue :
2
Database :
MEDLINE
Journal :
Mathematical biosciences and engineering : MBE
Publication Type :
Academic Journal
Accession number :
35135245
Full Text :
https://doi.org/10.3934/mbe.2022100