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- W2020919843 abstract "This paper provides a new regularization method for sparse representation based on a fixed-point iteration schema which combines two Lipschitzian-type mappings, a nonlinear one aimed to uniformly enhance the sparseness level of a candidate solution and a linear one which projects back into the feasible space of solutions. It is shown that this strategy locally minimizes a problem whose objective function falls into the class of the lp-norm and represents an efficient approximation of the intractable problem focusing on the l0-norm. Numerical experiments on randomly generated signals using classical stochastic models show better performances of the proposed technique with respect to a wide collection of well known algorithms for sparse representation." @default.
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- W2020919843 date "2011-12-01" @default.
- W2020919843 modified "2023-09-26" @default.
- W2020919843 title "Sparsity recovery by iterative orthogonal projections of nonlinear mappings" @default.
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- W2020919843 doi "https://doi.org/10.1109/isspit.2011.6151555" @default.
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