High-performance singular value decomposition

被引：0

作者：

Skillicorn, DB ^{[1
]}

Yang, XL ^{[1
]}

机构：

[1] Queens Univ, Dept Comp & Informat Sci, Kingston, ON K7L 3N6, Canada

来源：

DATA MINING FOR SCIENTIFIC AND ENGINEERING APPLICATIONS | 2001年 / 2卷

关键词：

dimensionality reduction; SVD; incremental algorithms; parallel computing; cost modeling;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Singular value decomposition is, among other things, a dimensionality reduction technique. It is used in data mining as a way to improve similarity measurements and as a preprocessing step before automatic clustering. We present several parallel algorithms for computing the SVD of a large matrix. Given a matrix with n rows and m columns, p-fold speedup of the computation part is achieved. Communication overheads range from phi (m(2)) to phi (pm(2)), which is smaller than the communication overheads of techniques based on parallelizing the inner loop of Hestenes-style algorithms.

引用

页码：401 / 424

页数：24

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