Matching Pursuits with random sequential subdictionaries

作者：Moussallam Manuel^*; Daudet Laurent; Richard Gael

来源：Signal Processing, 2012, 92(10): 2532-2544.

DOI：10.1016/j.sigpro.2012.03.019

• Matching Pursuits
• Random dictionaries
• Sparse approximation
• Audio signal compression

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.

• 出版日期2012-10