PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sparse recovery by thresholded non-negative least squares
Martin Slawski and Matthias Hein
NIPS 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 thresholding, experimentally even outperforming l1- regularization. Since NNLS also circumvents the delicate choice of a regularization parameter, our findings suggest that NNLS may be the method of choice.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:8708
Deposited By:Matthias Hein
Deposited On:21 February 2012