Theoretical analysis proves exponential convergence of matching pursuit in quasi-incoherent dictionaries, highlighting guaranteed error bounds comparable to optimal sparse approximations.
The purpose of this correspondence is to extend results by Villemoes and Temlyakov about exponential convergence of Matching Pursuit (MP) with some structured dictionaries for "simple" functions in finite or infinite dimension. The results are based on an extension of Tropp's results about Orthogonal Matching Pursuit (OMP) in finite dimension, with the observation that it does not only work for OMP but also for MP. The main contribution is a detailed analysis of the approximation and stability properties of MP with quasi-incoherent dictionaries, and a bound on the number of steps sufficient to reach an error no larger than a penalization factor times the best m-term approximation error.
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Gribonval et al. (2005) studied this question.
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