Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
411409 | Neurocomputing | 2016 | 7 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yu Zhang, Guoxu Zhou, Qibin Zhao, Andrzej Cichocki, Xingyu Wang,