文献类型：期刊文献

英文题名：Fast nonnegative tensor factorization based on accelerated proximal gradient and low-rank approximation

作者：Zhang, Yu[1]; Zhou, Guoxu[2,3]; Zhao, Qibin[2]; Cichocki, Andrzej[3,4]; Wang, Xingyu[1]

机构：[1] Key Laboratory for Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Automation, Guangdong University of Technology, Guangzhou, 510006, China; [3] Laboratory for Advanced Brain Signal Processing, RIKEN Brain Science Institute, Wako-shim, Saitama, 351-0198, Japan; [4] Skolkowo Institute of Science and Technology, Moscow, Russia

年份：2016

卷号：198

起止页码：148

外文期刊名：Neurocomputing

收录：EI(收录号：20161202134194)

语种：英文

外文关键词：Approximation algorithms - Tensors - Approximation theory - Factorization

摘要：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. ? 2016 Elsevier B.V.