0 norm based algorithms have numerous potential applications where a sparse signal is recovered from a small number of measurements. The direct l⁰ norm optimization problem is NP-hard. In this paper we work with the the smoothed l⁰(SL0) approximation algorithm for sparse representation. We give an upper bound on the run-time estimation error. This upper bound is tighter than the previously known bound. Subsequently, we develop a reliable stopping criterion. This criterion is helpful in avoiding the problems due to the underlying discontinuities of the l⁰ cost function. Furthermore, we propose an alternative optimization strategy, which results in a Newton like algorithm.">

An Improved Smoothed $\ell^0$ Approximation Algorithm for Sparse Representation (original) (raw)

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