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

作者：Zhang, Yu; Zhou, Guoxu^*; Zhao, Qibin; Cichocki, Andrzej; Wang, Xingyu

来源：Neurocomputing, 2016, 198: 148-154.

DOI：10.1016/j.neucom.2015.08.122

• CP (PARAFAC) decompositions
• Nonnegative tensor factorization
• Accelerated proximal gradient
• 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, 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.

• 出版日期2016-7-19
• 单位华东理工大学; 广东工业大学