A fast non-smooth nonnegative matrix factorization for learning sparse representation

Yang, Zuyuan, Zhang, Yu, Yan, Wei, Xiang, Yong and Xie, Shengli 2016, A fast non-smooth nonnegative matrix factorization for learning sparse representation, IEEE access, vol. 4, pp. 5161-5168, doi: 10.1109/ACCESS.2016.2605704.

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Title A fast non-smooth nonnegative matrix factorization for learning sparse representation
Author(s) Yang, Zuyuan
Zhang, Yu
Yan, Wei
Xiang, YongORCID iD for Xiang, Yong orcid.org/0000-0003-3545-7863
Xie, Shengli
Journal name IEEE access
Volume number 4
Start page 5161
End page 5168
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2016
ISSN 2169-3536
Keyword(s) Nonnegative matrix factorization
sparse representation
nonlinear convergence rate
Summary Nonnegative matrix factorization (NMF) is a hot topic in machine learning and data processing. Recently, a constrained version, non-smooth NMF (NsNMF), shows a great potential in learning meaningful sparse representation of the observed data. However, it suffers from a slow linear convergence rate, discouraging its applications to large-scale data representation. In this paper, a fast NsNMF (FNsNMF) algorithm is proposed to speed up NsNMF. In the proposed method, it first shows that the cost function of the derived sub-problem is convex and the corresponding gradient is Lipschitz continuous. Then, the optimization to this function is replaced by solving a proximal function, which is designed based on the Lipschitz constant and can be solved through utilizing a constructed fast convergent sequence. Due to the usage of the proximal function and its efficient optimization, our method can achieve a nonlinear convergence rate, much faster than NsNMF. Simulations in both computer generated data and the real-world data show the advantages of our algorithm over the compared methods.
Language eng
DOI 10.1109/ACCESS.2016.2605704
Field of Research 090609 Signal Processing
Socio Economic Objective 890104 Mobile Telephone Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30087587

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