Computer Science – Data Structures and Algorithms
Scientific paper
2011-07-13
Computer Science
Data Structures and Algorithms
20 pages - accepted 2nd April 2012 at Elsevier Signal Processing
Scientific paper
10.1016/j.sigpro.2012.03.019
Matching pursuits are a class of greedy algorithms commonly used in signal processing, for solving the sparse approximation problem. They rely on an atom selection step that requires the calculation of numerous projections, which can be computationally costly for large dictionaries and burdens their competitiveness in coding applications. We propose using a non adaptive random sequence of subdictionaries in the decomposition process, thus parsing a large dictionary in a probabilistic fashion with no additional projection cost nor parameter estimation. A theoretical modeling based on order statistics is provided, along with experimental evidence showing that the novel algorithm can be efficiently used on sparse approximation problems. An application to audio signal compression with multiscale time-frequency dictionaries is presented, along with a discussion of the complexity and practical implementations.
Daudet Laurent
Moussallam Manuel
Richard Gaël
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