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Cited by 9,112

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Matching pursuits with time-frequency dictionaries

Mallat, S.G., Zhifeng Zhang·1993·IEEE Transactions on Signal Processing
DOI 10.1109/78.258082
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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).>

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Metadata

Authors
Mallat, S. G., & Zhang, Z.
Venue
IEEE Transactions on Signal Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1993
Volume
41
Issue
12
Pages
3397-3415
ISSN
1053-587X (ISSN-L)
DOI
10.1109/78.258082
OpenAlex
W2151693816
Cited by
9,112
References
4
FWCI
45.99
Type
journal article
Language
en
Open access
closed

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