Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation

Nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, most of the existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. In this study...

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Vydané v:Neurocomputing (Amsterdam) Ročník 198; s. 148 - 154
Hlavní autori: Zhang, Yu, Zhou, Guoxu, Zhao, Qibin, Cichocki, Andrzej, Wang, Xingyu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 19.07.2016
Predmet:
Accelerated proximal gradient
Algorithms
Approximation
Computational efficiency
Convergence
CP (PARAFAC) decompositions
Data processing
Factorization
Low-rank approximation
Mathematical analysis
Nonnegative tensor factorization
Tensors
Nonnegative tensor factorization
Low-rank approximation
CP (PARAFAC) decompositions
Accelerated proximal gradient
ISSN:0925-2312, 1872-8286
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Shrnutí:Nonnegative tensor factorization (NTF) has been widely applied in high-dimensional nonnegative tensor data analysis. However, most of the existing algorithms suffer from slow convergence caused by the nonnegativity constraint and hence their practical applications are severely limited. In this study, we propose a new algorithm called FastNTF_APG to speed up NTF by combining accelerated proximal gradient and low-rank approximation. Experimental results demonstrate that FastNTF_APG achieves significantly higher computational efficiency than state-of-the-art NTF algorithms.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2015.08.122