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15 March 1994 Adaptive time-frequency decompositions with matching pursuit
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Abstract
To compute the optimal expansion of signals in redundant dictionary of waveforms is an NP complete problem. We introduce a greedy algorithm, called matching pursuit, that performs a suboptimal expansion. The waveforms are chosen iteratively in order to best match the signal structures. Matching pursuits are general procedures to compute adaptive signal representations. With a dictionary of Gabor functions, a matching pursuit defines an adaptive time-frequency transform. We derive a signal energy distribution in the time-frequency plane, which does not include interference terms, unlike Wigner and Cohen class distributions. A matching pursuit is a chaotic map, whose attractor defines a generic noise with respect to the dictionary. We derive an algorithm that isolates the coherent structures of a signal and an application to pattern extraction from noisy signals is described.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Geoffrey M. Davis, Stephane G. Mallat, and Zhifeng Zhang "Adaptive time-frequency decompositions with matching pursuit", Proc. SPIE 2242, Wavelet Applications, (15 March 1994); https://doi.org/10.1117/12.170041
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