Cyclic Matching Pursuits with Multiscale Time-frequency Dictionaries

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Resumé

We generalize cyclic matching pursuit (CMP),
propose an orthogonal variant,
and examine their performance using multiscale time-frequency dictionaries
in the sparse approximation of signals.
Overall, we find that the cyclic approach of CMP
produces signal models that have a much lower approximation error
than existing greedy iterative descent methods
such as matching pursuit (MP),
and are competitive with models found using orthogonal MP (OMP),
and orthogonal least squares (OLS).
This implies that CMP is a strong alternative to
the more computationally complex approaches of OMP and OLS
for modeling high-dimensional signals.
OriginalsprogEngelsk
TidsskriftAsilomar Conference on Signals, Systems and Computers. Conference Record
Sider (fra-til)581-585
ISSN1058-6393
DOI
StatusUdgivet - 2010
Begivenhed44th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, USA
Varighed: 7 nov. 201010 nov. 2010

Konference

Konference44th Asilomar Conference on Signals, Systems and Computers
LandUSA
ByPacific Grove
Periode07/11/201010/11/2010

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title = "Cyclic Matching Pursuits with Multiscale Time-frequency Dictionaries",
abstract = "We generalize cyclic matching pursuit (CMP),propose an orthogonal variant,and examine their performance using multiscale time-frequency dictionariesin the sparse approximation of signals.Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximation errorthan existing greedy iterative descent methodssuch as matching pursuit (MP),and are competitive with models found using orthogonal MP (OMP), and orthogonal least squares (OLS).This implies that CMP is a strong alternative to the more computationally complex approaches of OMP and OLSfor modeling high-dimensional signals.",
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Cyclic Matching Pursuits with Multiscale Time-frequency Dictionaries. / Sturm, Bob L.; Christensen, Mads Græsbøll.

I: Asilomar Conference on Signals, Systems and Computers. Conference Record, 2010, s. 581-585.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

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N2 - We generalize cyclic matching pursuit (CMP),propose an orthogonal variant,and examine their performance using multiscale time-frequency dictionariesin the sparse approximation of signals.Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximation errorthan existing greedy iterative descent methodssuch as matching pursuit (MP),and are competitive with models found using orthogonal MP (OMP), and orthogonal least squares (OLS).This implies that CMP is a strong alternative to the more computationally complex approaches of OMP and OLSfor modeling high-dimensional signals.

AB - We generalize cyclic matching pursuit (CMP),propose an orthogonal variant,and examine their performance using multiscale time-frequency dictionariesin the sparse approximation of signals.Overall, we find that the cyclic approach of CMP produces signal models that have a much lower approximation errorthan existing greedy iterative descent methodssuch as matching pursuit (MP),and are competitive with models found using orthogonal MP (OMP), and orthogonal least squares (OLS).This implies that CMP is a strong alternative to the more computationally complex approaches of OMP and OLSfor modeling high-dimensional signals.

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M3 - Conference article in Journal

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JO - Asilomar Conference on Signals, Systems and Computers. Conference Record

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