Sparse approximation with adaptive dictionary for image prediction
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Sparse and silent coding in neural circuits
Neurocomputing
Matching Pursuits with random sequential subdictionaries
Signal Processing
Recovering non-negative and combined sparse representations
Digital Signal Processing
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We propose a variant of Orthogonal Matching Pursuit (OMP), called LoCOMP, for scalable sparse signal approximation. The algorithm is designed for shift-invariant signal dictionaries with localized atoms, such as time-frequency dictionaries, and achieves approximation performance comparable to OMP at a computational cost similar to Matching Pursuit. Numerical experiments with a large audio signal show that, compared to OMP and Gradient Pursuit, the proposed algorithm runs in over 500 less time while leaving the approximation error almost unchanged.