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New Improved Algorithms for Compressive Sensing Based on lp Norm
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DOI：10.1109/TCSII.2013.2296133.png)
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Abstract
En 中文
A new algorithm for the reconstruction of sparse signals, which is referred to as the l(p)-regularized least squares (l(p)-RLS) algorithm, is proposed. The new algorithm is based on the minimization of a smoothed l(p)-norm regularized square error with p < 1. It uses a conjugate-gradient (CG) optimization method in a sequential minimization strategy that involves a two-parameter continuation technique. An improved version of the new algorithm is also proposed, which entails a bisection technique that optimizes an inherent regularization parameter. Extensive simulation results show that the new algorithm offers improved signal reconstruction performance and requires reduced computational effort relative to several state-of-the-art competing algorithms. The improved version of the l(p)-RLS algorithm offers better performance than the basic version, although this is achieved at the cost of increased computational effort.
Keywords:
Compressive sensing (CS)
conjugate-gradient (CG) optimization
least squares optimization
sequential optimization
l(p)-norm
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