A DISTRIBUTED AND INCREMENTAL SVD ALGORITHM FOR AGGLOMERATIVE DATA ANALYSIS ON LARGE NETWORKS

被引：30
|
作者：
Iwen, M. A. [1 ,2 ]
Ong, B. W. [3 ]
机构：
[1] Michigan State Univ, Dept Math, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Elect & Comp Engn, E Lansing, MI 48824 USA
[3] Michigan Technol Univ, Dept Math Sci, Houghton, MI 49931 USA
来源：
SIAM JOURNAL ON MATRIX ANALYSIS AND APPLICATIONS | 2016年 / 37卷 / 04期
基金：
美国国家科学基金会;
关键词：
singular value decomposition; low-rank approximations; distributed computing; incremental SVD; SINGULAR-VALUE DECOMPOSITION; UPDATING ALGORITHM; PARALLEL; COMMUNICATION;
D O I：
10.1137/16M1058467
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
In this paper it is shown that the SVD of a matrix can be constructed efficiently in a hierarchical approach. The proposed algorithm is proven to recover the singular values and left singular vectors of the input matrix A if its rank is known. Further, the hierarchical algorithm can be used to recover the d largest singular values and left singular vectors with bounded error. It is also shown that the proposed method is stable with respect to round-off errors or corruption of the original matrix entries. Numerical experiments validate the proposed algorithms and parallel cost analysis.
引用
收藏
页码：1699 / 1718
页数：20
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