Selecting the rank of truncated SVD by maximum approximation capacity

@article{Frank2011SelectingTR,
  title={Selecting the rank of truncated SVD by maximum approximation capacity},
  author={M. Frank and J. Buhmann},
  journal={2011 IEEE International Symposium on Information Theory Proceedings},
  year={2011},
  pages={1036-1040}
}
  • M. Frank, J. Buhmann
  • Published 2011
  • Mathematics, Computer Science
  • 2011 IEEE International Symposium on Information Theory Proceedings
  • Truncated Singular Value Decomposition (SVD) calculates the closest rank-k approximation of a given input matrix. Selecting the appropriate rank k defines a critical model order choice in most applications of SVD. To obtain a principled cut-off criterion for the spectrum, we convert the underlying optimization problem into a noisy channel coding problem. The optimal approximation capacity of this channel controls the appropriate strength of regularization to suppress noise. In simulation… CONTINUE READING
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