Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices
Statistics Theory
2014-05-05 v1 Statistics Theory
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.
Keywords
Cite
@article{arxiv.1405.0338,
title = {Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices},
author = {Dan Yang and Zongming Ma and Andreas Buja},
journal= {arXiv preprint arXiv:1405.0338},
year = {2014}
}