Corpus ID: 167217643

Adaptive Reduced Rank Regression

@article{Wu2020AdaptiveRR,
  title={Adaptive Reduced Rank Regression},
  author={Qiong Wu and F. M. F. Wong and Zhenming Liu and Yanhua Li and Varun Kanade},
  journal={ArXiv},
  year={2020},
  volume={abs/1905.11566}
}
Low rank regression has proven to be useful in a wide range of forecasting problems. However, in settings with a low signal-to-noise ratio, it is known to suffer from severe overfitting. This paper studies the reduced rank regression problem and presents algorithms with provable generalization guarantees. We use adaptive hard rank-thresholding in two different parts of the data analysis pipeline. First, we consider a low rank projection of the data to eliminate the components that are most… Expand
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References

SHOWING 1-10 OF 75 REFERENCES
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Reduced rank regression via adaptive nuclear norm penalization.
Bayesian sparse reduced rank multivariate regression
Reduced rank ridge regression and its kernel extensions
Reduced-rank regression for the multivariate linear model
On Robustness of Principal Component Regression
Optimal selection of reduced rank estimators of high-dimensional matrices
Low-Rank Graph-Regularized Structured Sparse Regression for Identifying Genetic Biomarkers
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