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Abstract

We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-Hoeffding bounds. Despite the simplicity, the approximation is comparable and sometimes better than previous work.

Our algorithm computes the sparse matrix approximation in a single pass over the data. Further, most of the entries in the output matrix are quantized, and can be succinctly represented by a bit vector, thus leading to much savings in space.

Keywords

Error Parameter Input Matrix Unit Eigenvector Eigenvector Computation Lanczos Iteration 
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-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sanjeev Arora
    • 1
  • Elad Hazan
    • 1
  • Satyen Kale
    • 1
  1. 1.Computer Science DepartmentPrinceton UniversityPrincetonUSA

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