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Robust Singular Value Decomposition BaLsed on Weighted Least Absolute Deviation Regression

  • Jung, Kang-Mo (Department of Informatics & Statistics, Kunsan National University and Department of Statistics, University of California at Davis)
  • Received : 20100800
  • Accepted : 20101000
  • Published : 2010.11.30

Abstract

The singular value decomposition of a rectangular matrix is a basic tool to understand the structure of the data and particularly the relationship between row and column factors. However, conventional singular value decomposition used the least squares method and is not robust to outliers. We propose a simple robust singular value decomposition algorithm based on the weighted least absolute deviation which is not sensitive to leverage points. Its implementation is easy and the computation time is reasonably low. Numerical results give the data structure and the outlying information.

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