计算机工程与应用Issue(3)：6-12,45,8.DOI:10.3778/j.issn.1002-8331.1306-0274
分布式奇异值分解最小平方估计算法
Distributed singular value decomposition least squares estimation algorithm
摘要
Abstract
Singular Value Decomposition(SVD)for solving least-squares estimation problem is studied. This paper pro-poses Iterative Divide and Merge algorithm(IDMSVD), aims to improve the problem that singular value decomposition in the estimation of parameters is very time-consuming and memory space. Based on IDMSVD a distributed iterative split and merge algorithm(MRDSVD)is proposed, using Hadoop’s MapReduce platform to achieve. The experimental results show, IDMSVD can effectively improve the SVD least squares solution required run time and memory space consuming problem. MRDSVD algorithm can further improve the running time of IDMSVD.关键词
矩阵分解/奇异值分解/最小平方估计/大型数据集/分布式Key words
matrix decomposition/Singular Value Decomposition(SVD)/least-squares solution/large-scale dataset/distributed分类
信息技术与安全科学引用本文复制引用
李繁,金明录,刘继..分布式奇异值分解最小平方估计算法[J].计算机工程与应用,2014,(3):6-12,45,8.基金项目
国家自然科学基金（No.71261025）；教育部人文社科基金（No.11yjc630129）。 （）
