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On the statistics of matching pursuit angles
Signal Processing, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lisandro Lovisolo +2 more
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Cyclic adaptive matching pursuit
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012We present an improved Adaptive Matching Pursuit algorithm for computing approximate sparse solutions for overdetermined systems of equations. The algorithms use a greedy approach, based on a neighbor permutation, to select the ordered support positions followed by a cyclical optimization of the selected coefficients. The sparsity level of the solution
Alexandru Onose, Bogdan Dumitrescu
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Knowledge-enhanced Matching Pursuit
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013Compressive Sensing is possible when the sensing matrix acts as a near isometry on signals of interest that can be sparsely or compressively represented. The attraction of greedy algorithms such as Orthogonal Matching Pursuit is their simplicity. However they fail to take advantage of both the structure of the sensing matrix and any prior information ...
Yuejie Chi, A. Robert Calderbank
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A genetic matching pursuit algorithm
Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003Advanced signal processing (SP) methods often lead to greedy algorithms that are time consuming, so useless in real time applications. However, nowadays, efficient implementations of such algorithms become more and more feasible, eventually with the help of concepts withdrawn from fields outside SP.
Dan Stefanoiu, Florin Ionescu
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Matching pursuit filter design
Proceedings of the 12th IAPR International Conference on Pattern Recognition (Cat. No.94CH3440-5), 2002A method has been devised of using localized information to detect objects with varying signatures without prior segmentation. The detection is performed by a new class of nonlinear filters called matching pursuit filters, which are trained on multiple examples of the object of interest. Matching pursuit filters are designed through a generalization of
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Matching pursuit with damped sinusoids
1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002The matching pursuit algorithm derives an expansion of a signal in terms of the elements of a large dictionary of time-frequency 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 explored; it is shown that the expansion can be ...
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Adaptive Quantization for Matching Pursuit
2006 IEEE Workshop on Multimedia Signal Processing, 2006We propose an adaptive quantization scheme for matching pursuit. Different quantizers are used in different matching pursuit (MP) stages based on the probability distribution of MP coefficients. The quantizers are optimized for a given rate budget constraint.
Alireza Shoa, Shahram Shirani
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Oblique Projection Matching Pursuit
Mobile Networks and Applications, 2016Recent theory of compressed sensing (CS) tells us that sparse signals can be reconstructed from a small number of random samples. In reconstruction of sparse signals, greedy algorithms, such as the orthogonal matching pursuit (OMP), have been shown to be computationally efficient.
Jian Wang 0016 +3 more
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Random Matching Pursuit for Image Watermarking
IEEE Transactions on Circuits and Systems for Video Technology, 2019The classical solution to an underdetermined system of linear equations mainly has two opposite directions, which lead to either a large $\ell _{2}$ -norm sparse solution or a non-sparse minimum $\ell _{2}$ -norm solution. In this paper, we systematically show that by modifying the well-known basic matching pursuit algorithm originally ...
Guang Hua 0001 +4 more
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Refining Kernel Matching Pursuit
2010Kernel matching pursuit (KMP), as a greedy machine learning algorithm, appends iteratively functions from a kernel-based dictionary to its solution An obvious problem is that all kernel functions in dictionary will keep unchanged during the whole process of appending It is difficult, however, to determine the optimal dictionary of kernel functions ...
Jianwu Li, Yao Lu 0001
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