Near-optimal column-based matrix reconstruction

  • Boutsidis C
  • Drineas P
  • Magdon-Ismail M
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We consider low-rank reconstruction of a matrix using its columns and we present asymptotically optimal algorithms for both spectral norm and Frobenius norm reconstruction. The main tools we introduce to obtain our r esults are: (i) the use of fast approximate SVD-like decompositions for column reconstruction, and (ii) two deter ministic algorithms for selecting rows from matrices with orthonormal columns, building upon the sparse represen tation theorem for decompositions of the identity that appeared in \cite{BSS09}.

Author-supplied keywords

  • 1
  • 10
  • 1137
  • 11k45
  • 12086755x
  • 15a18
  • 15b52
  • a
  • ams subject classifications
  • approximation to a matrix
  • doi
  • introduction
  • low
  • low-rank approximations
  • n is
  • numerical linear algebra
  • r m
  • randomized algorithms
  • the best rank- k

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  • Christos Boutsidis

  • Petros Drineas

  • M Magdon-Ismail

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