You are not logged in.

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.

Attached Files
Name Description MIMEType Size Downloads

Title A fast non-smooth nonnegative matrix factorization for learning sparse representation
Author(s) Yang, Zuyuan
Zhang, Yu
Yan, Wei
Xiang, Yong
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

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 0 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 57 Abstract Views, 0 File Downloads  -  Detailed Statistics
Created: Mon, 17 Oct 2016, 08:19:54 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.