中国计量大学学报2017，Vol.28Issue(1)：119-125,7.DOI:10.3969/j.issn.2096-2835.2017.01.021
非负矩阵分解的分布式算法
A distributed learning algorithm for nonnegative matrix factorization
摘要
Abstract
A distributed learning algorithm is put forward for dissolving the factorization of large-scale nonnegative matrixes..The factorization of nonnegative matrixes is a hot problem in this field with many effective algorithms.However,the large-scale nonnegative matrixes,there have not been any highly valid algorithms.We combined the distributed concept and the parallel computing with the traditional matrix factorization methods to develop a distributed learning algorithm for complex nonnegative matrix factorization.The simulation experiments show that the proposed algorithm is more efficient and faster than the traditional distributed learning algorithm and matrix factorization methods.关键词
大规模非负矩阵/矩阵分解/分布式学习算法/并行式计算Key words
large-scale nonnegative matrix/matrix factorization/distributed learning algorithm/parallel computing分类
信息技术与安全科学引用本文复制引用
徐富盛,曹飞龙..非负矩阵分解的分布式算法[J].中国计量大学学报,2017,28(1):119-125,7.基金项目
国家自然科学基金资助项目(No.61674277,91330118). （No.61674277,91330118）
