Iterative Hard Thresholding and L0 Regularisation - Blumensath, T.; Yaghoobi, M.; Davies, M.E. (2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07, 2007)
Sparse signal approximations are approximations that use only a small number of elementary waveforms to describe a signal. In this paper we proof the convergence of an iterative hard thresholding algorithm and show, that the fixed points of that algorithm are local minima of the sparse approximation cost function, which measures both, the reconstruction error and the number of elements in the representation. Simulation results suggest that the algorithm is comparable in performance to a commonly used alternative method.