A low complexity orthogonal matching pursuit for sparse signal approximation with shift-invariant dictionaries

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.


Presented at:
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP09), Taipei, Taiwan, 2009
Year:
2009
Publisher:
Taipei, Taiwan
Keywords:
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 Record created 2009-01-20, last modified 2018-09-13

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