文献类型：会议论文

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]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Guangdong Univ Technol, Sch Automat, Guangzhou 510006, Guangdong, Peoples R China;[3]RIKEN, Brain Sci Inst, Lab Adv Brain Signal Proc, 2-1 Hirosawa, Wako, Saitama 3510198, Japan;[4]Skolkowo Inst Sci & Technol, Moscow, Russia

会议论文集：11th International Symposium on Neural Networks (ISNN)

会议日期：NOV 28-DEC 01, 2014

会议地点：PEOPLES R CHINA

语种：英文

外文关键词：CP (PARAFAC) decompositions; Nonnegative tensor factorization; Accelerated proximal gradient; Low-rank approximation

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. (C) 2016 Elsevier B.V. All rights reserved.