ML Anthology

Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices

Dan Yang, Zongming Ma, Andreas Buja
JMLR 2016 pp. 1-27
/jmlr/2016/yang2016jmlr-rate/

Abstract

We study minimax rates for denoising simultaneously sparse and low rank matrices in high dimensions. We show that an iterative thresholding algorithm achieves (near) optimal rates adaptively under mild conditions for a large class of loss functions. Numerical experiments on synthetic datasets also demonstrate the competitive performance of the proposed method.

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Cite

Text

Yang et al. "Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices." Journal of Machine Learning Research, 2016.

Markdown

[Yang et al. "Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices." Journal of Machine Learning Research, 2016.](https://mlanthology.org/jmlr/2016/yang2016jmlr-rate/)

BibTeX

@article{yang2016jmlr-rate,
  title     = {{Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices}},
  author    = {Yang, Dan and Ma, Zongming and Buja, Andreas},
  journal   = {Journal of Machine Learning Research},
  year      = {2016},
  pages     = {1-27},
  volume    = {17},
  url       = {https://mlanthology.org/jmlr/2016/yang2016jmlr-rate/}
}