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Sparse recovery by thresholded non-negative least squares
Martin Slawski · Matthias Hein

Wed Dec 14 08:45 AM -- 02:59 PM (PST) @ None #None

Non-negative data are commonly encountered in numerous fields, making non-negative 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.

Author Information

Martin Slawski (Saarland University)
Matthias Hein (University of Tübingen)

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