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Singular Value Decomposition

  • Kenneth Lange
Chapter
Part of the Statistics and Computing book series (SCO)

Abstract

In many modern applications involving large data sets, statisticians are confronted with a large m×n matrix X = (x ij) that encodes n features on each of mobjects. For instance, in gene microarray studies x ij represents the expression level of the ith gene under the jth experimental condition [13]. In information retrieval, x ij represents the frequency of the jth word or term in the ith document [2]. The singular value decomposition (svd) captures the structure of such matrices. In many applications there are alternatives to the svd, but these are seldom as informative or as numerically accurate.

Keywords

Diagonal Entry Singular Vector Ridge Regression Polar Decomposition Full Column Rank 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer New York 2010

Authors and Affiliations

  1. 1.Departments of Biomathematics, Human Genetics, and Statistics David Geffen School of MedicineUniversity of California, Los AngelesLos AngelesUSA

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