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Matching pursuits with timefrequency dictionaries
 IEEE Transactions on Signal Processing
, 1993
"... AbstractWe 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 t ..."
Abstract

Cited by 1654 (13 self)
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AbstractWe 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
Matching Pursuit: Parametrization Matters
"... Matching Pursuit (MP) : iterative/greedy way to decompose ..."
Matching Pursuit
, 1993
"... This paper presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tensor product reproducing kernel Hilbert space, we decompose the log likelihood into the sum of different functional components suc ..."
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Cited by 10 (0 self)
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This paper presents a nonparametric penalized likelihood approach for variable selection and model building, called likelihood basis pursuit (LBP). In the setting of a tensor product reproducing kernel Hilbert space, we decompose the log likelihood into the sum of different functional components
Signal recovery from random measurements via Orthogonal Matching Pursuit
 IEEE TRANS. INFORM. THEORY
, 2007
"... This technical report demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over previous ..."
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Cited by 780 (9 self)
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This technical report demonstrates theoretically and empirically that a greedy algorithm called Orthogonal Matching Pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal. This is a massive improvement over
Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition
 in Conference Record of The TwentySeventh Asilomar Conference on Signals, Systems and Computers
, 1993
"... In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang (199 ..."
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Cited by 622 (1 self)
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In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries of elementary building blocks e.g. aiEne (wa.velet) frames. We propoeea modification to the Matching Pursuit algorithm of Mallat and Zhang
Kernel matching pursuit
 Machine Learning
, 2002
"... Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the leastsquares sense. We show how matching pursuit can be extended to use nonsquared error loss functions, a ..."
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Cited by 86 (0 self)
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Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target function in the leastsquares sense. We show how matching pursuit can be extended to use nonsquared error loss functions
Matching Pursuit With Damped Sinusoids
 In Proc. ICASSP
, 1997
"... The matching pursuit algorithm derives an expansion of a signal in terms of the elements of a large dictionary of timefrequency atoms. This paper considers the use of matching pursuit for computing signal expansions in terms of damped sinusoids. First, expansion based on complex damped sinusoids is ..."
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Cited by 31 (3 self)
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The matching pursuit algorithm derives an expansion of a signal in terms of the elements of a large dictionary of timefrequency atoms. This paper considers the use of matching pursuit for computing signal expansions in terms of damped sinusoids. First, expansion based on complex damped sinusoids
Theory of matching pursuit
"... We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the KPCA bound of ShaweTaylor et al [7] and highly predictive of the size of the subspace needed to cap ..."
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Cited by 8 (3 self)
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We analyse matching pursuit for kernel principal components analysis (KPCA) by proving that the sparse subspace it produces is a sample compression scheme. We show that this bound is tighter than the KPCA bound of ShaweTaylor et al [7] and highly predictive of the size of the subspace needed
MATCHING PURSUIT WITH STOCHASTIC SELECTION
"... In this paper, we propose a Stochastic Selection strategy that accelerates the atom selection step of Matching Pursuit. This strategy consists of selecting randomly a subset of atoms and a subset of rows in the full dictionary at each step of the Matching Pursuit to obtain a suboptimal but fast ato ..."
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In this paper, we propose a Stochastic Selection strategy that accelerates the atom selection step of Matching Pursuit. This strategy consists of selecting randomly a subset of atoms and a subset of rows in the full dictionary at each step of the Matching Pursuit to obtain a suboptimal but fast
AMP: ASSEMBLY MATCHING PURSUIT
"... Pacific Symposium on Biocomputing 2013 session Personalized medicine: from genotypes and molecular phenotypes towards therapy. The paper contains contains original, unpublished results, and is not currently under consideration elsewhere. All coauthors concur with the contents of the paper. AMP: ASS ..."
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