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Robust sparse recovery with non-negativity constraints
Matthias Hein
Workshop on Signal Processing with Adaptive Sparse Structured Representations (SPARS)
2011.
AbstractIt has been established recently that sparse non-negative
signals can be recovered using non-negativity constraints only. This
result is obtained within an idealized setting of exact sparsity and
absence of noise. We propose non-negative least squares − without any
regularization − followed by thresholding for the noisy case. We develop
conditions under which one can prove a finite sample result for support
recovery and tackle the case of an approximately sparse target. Under
weaker conditions, we show that non-negative least squares is consistent
for prediction. As illustration, we present a feature extraction problem
from Proteomics. [Edit]
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