OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage

@article{Nadakuditi2014OptShrinkAA,
  title={OptShrink: An Algorithm for Improved Low-Rank Signal Matrix Denoising by Optimal, Data-Driven Singular Value Shrinkage},
  author={Raj Rao Nadakuditi},
  journal={IEEE Transactions on Information Theory},
  year={2014},
  volume={60},
  pages={3002-3018}
}
  • R. Nadakuditi
  • Published 25 June 2013
  • Computer Science
  • IEEE Transactions on Information Theory
The truncated singular value decomposition of the measurement matrix is the optimal solution to the representation problem of how to best approximate a noisy measurement matrix using a low-rank matrix. Here, we consider the (unobservable) denoising problem of how to best approximate a low-rank signal matrix buried in noise by optimal (re)weighting of the singular vectors of the measurement matrix. We exploit recent results from random matrix theory to exactly characterize the large matrix limit… 

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