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Panhellenic Conference on Informatics

PCI 2001: Advances in Informatics pp 279–296Cite as

An Experimental Evaluation of a Monte-Carlo Algorithm for Singular Value Decomposition

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2563))

Abstract

We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values ofa matrix A, is not only oft heoretical interest but also a fast, viable alternative to traditional algorithms. The algorithm samples a small number ofro ws (or columns) oft he matrix, scales them appropriately to form a small matrix S and computes the singular value decomposition (SVD) of S, which is a good approximation to the SVD ofthe original matrix. We experimentally evaluate the accuracy and speed oft his randomized algorithm using image matrices and three different sampling schemes. Our results show that our approximations oft he singular vectors of A span almost the same space as the corresponding exact singular vectors of A.

A preliminary version of his work appeared in the 2001 Panhellenic Conference on Informatics [5].

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Author information

Authors and Affiliations

  1. Computer Science Department, Yale University, 06520, New Haven, CT, USA

    Petros Drineas & Patrick S. Huggins

  2. Division of Engineering and Applied Sciences, Harvard University, 02138, Cambridge, MA, USA

    Eleni Drinea

Authors
  1. Petros Drineas
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  2. Eleni Drinea
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  3. Patrick S. Huggins
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Editor information

Editors and Affiliations

  1. Dept. of Informatics, Aristotle University, 54006, Thessaloniki, Greece

    Yannis Manolopoulos

  2. Dept. of Computer Science, University of Cyprus, P.O. Box 20537, 1678, Nicosia, Cyprus

    Skevos Evripidou  & Antonis C. Kakas  & 

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© 2003 Springer-Verlag Berlin Heidelberg

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Drineas, P., Drinea, E., Huggins, P.S. (2003). An Experimental Evaluation of a Monte-Carlo Algorithm for Singular Value Decomposition. In: Manolopoulos, Y., Evripidou, S., Kakas, A.C. (eds) Advances in Informatics. PCI 2001. Lecture Notes in Computer Science, vol 2563. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-38076-0_19

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  • DOI: https://doi.org/10.1007/3-540-38076-0_19

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-07544-8

  • Online ISBN: 978-3-540-38076-4

  • eBook Packages: Springer Book Archive

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