High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity

@inproceedings{Loh2011HighdimensionalRW,
  title={High-dimensional regression with noisy and missing data: Provable guarantees with non-convexity},
  author={Po-Ling Loh and Martin J. Wainwright},
  booktitle={NIPS},
  year={2011}
}
Although the standard formulations of prediction problems involve fully-observed and noiseless data drawn in an i.i.d. manner, many applications involve noisy and/or missing data, possibly involving dependencies. We study these issues in the context of high-dimensional sparse linear regression, and propose novel estimators for the cases of noisy, missing, and/or dependent data. Many standard approaches to noisy or missing data, such as those using the EM algorithm, lead to optimization problems… CONTINUE READING
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