A DEIM Induced CUR Factorization

Abstract

We derive a CUR approximate matrix factorization based on the Discrete Empirical Interpolation Method (DEIM). For a given matrix A, such a factorization provides a low rank approximate decomposition of the form A nearly equal to CUR, where C and R are subsets of the columns and rows of A, and U is constructed to make CUR a good approximation. Given a low-rank singular value decomposition A nearly equal to VSWT , the DEIM procedure uses V and W to select the columns and rows of A that form C and R. Through an error analysis applicable to a general class of CUR factorizations, we show that the accuracy tracks the optimal approximation error within a factor that depends on the conditioning of submatrices of V and W. For very large problems, V and W can be approximated well using an incremental QR algorithm that makes only one pass through A. Numerical examples illustrate the favorable performance of the DEIM-CUR method compared to CUR approximations based on leverage scores.

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Document Details

Document Type
Technical Report
Publication Date
Sep 18, 2015
Accession Number
ADA625637

Entities

People

  • D. C. Sorensen
  • M. Embree

Organizations

  • Rice University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Applied Mathematics
  • Computer Science
  • Data Sets
  • Decomposition
  • Error Analysis
  • Errors
  • Interpolation
  • Linear Algebra
  • Mathematics
  • Notation
  • Probability
  • Probability Distributions
  • Random Variables
  • Sampling
  • Theoretical Computer Science

Fields of Study

  • Computer science

Readers

  • Linear Algebra
  • Neural Network Machine Learning.