一种用于大规模矩阵奇异值分解的分裂合并方法。
A Split-and-Merge Approach for Singular Value Decomposition of Large-Scale Matrices.
作者信息
Liang Faming, Shi Runmin, Mo Qianxing
机构信息
Department of Biostatistics, University of Florida, Gainesville, FL 32611,
Department of Statistics, University of Florida, Gainesville, FL 32611.
出版信息
Stat Interface. 2016;9(4):453-459. doi: 10.4310/SII.2016.v9.n4.a5. Epub 2016 Sep 14.
We propose a new SVD algorithm based on the split-and- merge strategy, which possesses an embarrassingly parallel structure and thus can be efficiently implemented on a distributed or multicore machine. The new algorithm can also be implemented in serial for online eigen-analysis. The new algorithm is particularly suitable for big data problems: Its embarrassingly parallel structure renders it usable for feature screening, while this has been beyond the ability of the existing parallel SVD algorithms.
我们提出了一种基于分割合并策略的新奇异值分解（SVD）算法，该算法具有易于并行的结构，因此可以在分布式或多核机器上高效实现。新算法也可以串行实现用于在线特征分析。新算法特别适用于大数据问题：其易于并行的结构使其可用于特征筛选，而这是现有并行SVD算法所无法做到的。
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