Sparse recovery by thresholded non-negative least squares

  title={Sparse recovery by thresholded non-negative least squares},
  author={Martin Slawski and Matthias Hein},
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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