Fast Matching Pursuit with Multi-Gabor Dictionaries

TL;DR

This paper introduces an accelerated matching pursuit algorithm tailored for multi-Gabor dictionaries, significantly speeding up sparse signal approximation with theoretical convergence guarantees and practical implementation in C, MATLAB, and Octave.

Contribution

It presents a novel acceleration technique for matching pursuit on multi-Gabor dictionaries, improving speed and efficiency over existing methods.

Findings

Up to 70 times faster than standard MPTK implementation.

Theoretical convergence of the approximate update algorithm.

Effective implementation with cross-platform interfaces.

Abstract

Finding the best K-sparse approximation of a signal in a redundant dictionary is an NP-hard problem. Suboptimal greedy matching pursuit (MP) algorithms are generally used for this task. In this work, we present an acceleration technique and an implementation of the matching pursuit algorithm acting on a multi-Gabor dictionary, i.e., a concatenation of several Gabor-type time-frequency dictionaries, each of which consisting of translations and modulations of a possibly different window and time and frequency shift parameters. The technique is based on pre-computing and thresholding inner products between atoms and on updating the residual directly in the coefficient domain, i.e., without the round-trip to the signal domain. Since the proposed acceleration technique involves an approximate update step, we provide theoretical and experimental results illustrating the convergence of the resulting algorithm. The implementation is written in C (compatible with C99 and C++11) and we also provide Matlab and GNU Octave interfaces. For some settings, the implementation is up to 70 times faster than the standard Matching Pursuit Toolkit (MPTK).