Abstract
The authors introduce an algorithm, called matching pursuit, that decomposes any signal into a linear expansion of waveforms that are selected from a redundant dictionary of functions. These waveforms are chosen 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. They 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 isolates the signal structures that are coherent with respect to a given dictionary. An application to pattern extraction from noisy signals is described. They compare a matching pursuit decomposition with a signal expansion over an optimized wavepacket orthonormal basis, selected with the algorithm of Coifman and Wickerhauser see (IEEE Trans. Informat. Theory, vol. 38, Mar. 1992).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Matching pursuitOrthonormal basisTime–frequency analysisMatching (statistics)SIGNAL (programming language)Computer scienceBasis pursuitAlgorithmWaveformSignal processingPattern recognition (psychology)Basis (linear algebra)Artificial intelligenceMathematicsSpeech recognitionCompressed sensingDigital signal processingComputer vision
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Publication Info
- Year
- 1993
- Type
- article
- Volume
- 41
- Issue
- 12
- Pages
- 3397-3415
- Citations
- 8998
- Access
- Closed
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Cite This
Stéphane Mallat,
Zhifeng Zhang
(1993).
Matching pursuits with time-frequency dictionaries.
IEEE Transactions on Signal Processing
, 41
(12)
, 3397-3415.
https://doi.org/10.1109/78.258082
Identifiers
- DOI
- 10.1109/78.258082