Sparse recovery by thresholded non-negative least squares

@inproceedings{Slawski2011SparseRB,
  title={Sparse recovery by thresholded non-negative least squares},
  author={Martin Slawski and Matthias Hein},
  booktitle={NIPS},
  year={2011}
}
Non-negative data are commonly encountered in numerous fields, making nonnegative least squares regression (NNLS) a frequently used tool. At least relative to its simplicity, it often performs rather well in practice. Serious doubts about its usefulness arise for modern high-dimensional linear models. Even in this setting − unlike first intuition may suggest − we show that for a broad class of designs, NNLS is resistant to overfitting and works excellently for sparse recovery when combined with… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 27 references

Sparse recovery for Protein Mass Spectrometry data

M. Slawski, M. Hein
NIPS workshop on practical applications of sparse modelling • 2010
View 2 Excerpts

Bühlmann . On the conditions used to prove oracle results for the Lasso

B. Yu
The Electronic Journal of Statistics • 2009

Conditions for a unique non-negative solution to an underdetermined system

2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton) • 2009
View 3 Excerpts

On the Consistency of Feature Selection using Greedy Least Squares Regression

T. Zhang
Journal of Machine Learning Research, 10:555–568 • 2009
View 2 Excerpts

On the conditions used to prove oracle results for the Lasso

S. van de Geer, P. Bühlmann
The Electronic Journal of Statistics, • 2009
View 2 Excerpts

Revisiting Marginal Regression

C. Genovese, J. Jin, L. Wasserman
Technical report, Carnegie Mellon University • 2009
View 1 Excerpt

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