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Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit

Karin C Knudson, Jacob L Yates, Alexander C Huk, JonathanW Pillow
Advances in Neural Information Processing Systems  
Orthogonal Matching Pursuit (OMP) algorithm [11].  ...  We test the resulting method, which we call Continuous Orthogonal Matching Pursuit (COMP), on simulated and neural data, where it shows gains over CBP in both speed and accuracy.  ...  In Section 2 we describe the method of Continuous Basis Pursuit (CBP), which our method builds upon. In Section 3 we develop our method, which we call Continuous Orthogonal Matching Pursuit (COMP).  ... 
pmid:25914512 pmcid:PMC4408936 fatcat:re4756epdveuve2sedt42eafsi

On-Grid DOA Estimation Method Using Orthogonal Matching Pursuit [article]

Abhishek Aich, P.Palanisamy
2018 arXiv   pre-print
In this paper, we introduce Orthogonal Matching Pursuit (OMP) to the DOA estimation problem.  ...  Direction of Arrival (DOA) estimation of multiple narrow-band coherent or partially coherent sources is a major challenge in array signal processing.  ...  The typical greedy sparse recovery approaches are basis pursuit (BP), compressive sampling matching pursuit (CoSaMP), orthogonal matching pursuit (OMP) and sparse Bayesian learning (SBL) [9] .  ... 
arXiv:1705.05211v2 fatcat:enfkkjdgz5aehl2pywckwbkt7y

Editorial

Farokh Marvasti, Ali Mohammad-Djafari, Jonathon Chambers
2012 EURASIP Journal on Advances in Signal Processing  
[11] also study sparse signal reconstruction and propose the restricted isometry property for the orthogonal matching pursuit algorithm.  ...  The precession frequency and the scattering centres of the missile are then estimated through nonlinear least squares and an orthogonal matching pursuit algorithm.  ... 
doi:10.1186/1687-6180-2012-90 fatcat:odv3jhtw35aopips5muxgblkja

Efficient Coding of Time-Relative Structure Using Spikes

Evan Smith, Michael S. Lewicki
2005 Neural Computation  
Part of the difficulty is that with standard block-based signal analysis methods, the representation is sensitive to the arbitrary alignment of the blocks with respect to the signal.  ...  We demonstrate the properties of this representation for the purpose of characterizing structure in various types of nonstationary acoustic signals.  ...  As a result, matching pursuit codes are composed of mutually orthogonal signal structures.  ... 
doi:10.1162/0899766052530839 pmid:15563746 fatcat:cevu67a3rfbydly4dwumgaehfe

Matching Reconstruction Algorithms Performance Comparison based on Compressed Sensing in GPR Imaging

Rong-xing Duan, Hui-lin Zhou, Gan-chun Zhu
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
So we may reasonably conclude that the regularized orthogonal matching pursuit algorithm has a better performance than the other matching algorithms.  ...  Furthermore, the performance of matching reconstruction algorithms under the different signal to noise ratios (SNR), measurement dimensions and sparseness values is also presented.  ...  pursuit [13] (STOMP), regularized orthogonal matching pursuit [14] (ROMP) and compressive sampling matching pursuit [15] (CoSaMP).  ... 
doi:10.14257/ijsip.2015.8.8.12 fatcat:dhi3e36difc5zlgmdo4xg4fsja

Feature detection using spikes: The greedy approach

Laurent Perrinet
2004 Journal of Physiology - Paris  
This algorithm is similar to 'Matching Pursuit' and mimics the parallel architecture of neural computations.  ...  This model uses an over-complete dictionary of primitives which provides a distributed probabilistic representation of input features.  ...  From this representation, we inferred the best match using the Bayes rule and an inference decision criterion.  ... 
doi:10.1016/j.jphysparis.2005.09.012 pmid:16310348 fatcat:4gahig4hk5g3zgzcatubp5eo7i

Dictionary learning with equiprobable matching pursuit

Fredrik Sandin, Sergio Martin-del-Campo
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
in the case of ordinary matching pursuit and orthogonal matching pursuit with shift-invariant dictionaries.  ...  Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-ofthe-art results in several practical signal processing applications.  ...  The dictionary learning problem is to infer the set of atomic waveforms, ϕ i , in the dictionary, Φ, so that the matching pursuit results in a sparse representation with low residual.  ... 
doi:10.1109/ijcnn.2017.7965902 dblp:conf/ijcnn/SandinC17 fatcat:2lo7oi3se5byxioielwlrvqw2y

Dictionary Learning with Equiprobable Matching Pursuit [article]

Fredrik Sandin, Sergio Martin-del-Campo
2016 arXiv   pre-print
in the case of ordinary matching pursuit and orthogonal matching pursuit with shift-invariant dictionaries.  ...  Sparse signal representations based on linear combinations of learned atoms have been used to obtain state-of-the-art results in several practical signal processing applications.  ...  The dictionary learning problem is to infer the set of atomic waveforms, ϕ i , in the dictionary, Φ, so that the matching pursuit results in a sparse representation with low residual.  ... 
arXiv:1611.09333v1 fatcat:zcctkautdjbsrk3lwi2rzpz43e

A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields

Martin Rehn, Friedrich T. Sommer
2006 Journal of Computational Neuroscience  
Computational models of primary visual cortex have demonstrated that principles of efficient coding and neuronal sparseness can explain the emergence of neurones with localised oriented receptive fields  ...  The model forms efficient visual representations in which the number of active neurones, rather than mean neuronal activity, is limited.  ...  Matching pursuit is a popular algorithm for the step by step refinement of signal representations in the field of adaptive signal processing; when run for a few iterations, it can form sparse-set codes  ... 
doi:10.1007/s10827-006-0003-9 pmid:17053994 fatcat:agy37zpptrg7xisjj2jq6d2jfe

Hierarchical Bayesian approach for jointly-sparse solution of multiple-measurement vectors

Mohammad Shekaramiz, Todd K. Moon, Jacob H. Gunther
2014 2014 48th Asilomar Conference on Signals, Systems and Computers  
Most of the previous work for finding such sparse representations are based on greedy and sub-optimal algorithms such as Basis Pursuit (BP), Matching Pursuit (MP), and Orthogonal Matching Pursuit (OMP)  ...  It is well-known that many signals of interest can be well-estimated via just a small number of supports under some specific basis.  ...  Solution for the sparsest measurement vector is practically achieved using greedy and sub-optimal algorithms such as Basis Pursuit (BP), Matching Pursuit (MP), and Orthogonal Matching Pursuit (OMP) [3  ... 
doi:10.1109/acssc.2014.7094813 pmid:29104413 pmcid:PMC5667949 fatcat:k5xgvlybdrbydnq3rdmhpyu6le

Continuous structure based Bayesian compressive sensing for sparse reconstruction of time-frequency distributions

Qisong Wu, Yimin D. Zhang, Moeness G. Amin
2014 2014 19th International Conference on Digital Signal Processing  
Compared with the existing sparse signal reconstruction techniques, the proposed technique achieves improved interpretation of the TFD, particularly when the signals are noisy or with missing samples.  ...  Among many applications of this class of representations are the two-dimensional timefrequency distributions (TFDs) of radar signals, which are often modeled as frequency modulated (FM) signals characterized  ...  CONTINUOUS STRUCTURE BAYESIAN COMPRESSIVE SENSING A number of inversion algorithms, such as the orthogonal matching pursuit (OMP) [16] , basis pursuit [21] , and sparse Bayesian learning [22] [23] [  ... 
doi:10.1109/icdsp.2014.6900783 dblp:conf/icdsp/WuZA14 fatcat:sh6ifge2z5g6bhfck4qej2y7n4

Sparse decomposition of transformation-invariant signals with continuous basis pursuit

Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
2011 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
We compare our method, which we call continuous basis pursuit (CBP) with the standard basis pursuit approach on a sparse deconvolution task.  ...  Consider the decomposition of a signal into features that undergo transformations drawn from a continuous family.  ...  (a) Basis pursuit (BP) dictionary consists of discretely shifted f 's. (b) Continuous basis pursuit with first-order Taylor interpolator (CBP-T).  ... 
doi:10.1109/icassp.2011.5947244 dblp:conf/icassp/EkanadhamTS11 fatcat:6gydfdudfzegvohrpruoimgi2a

SparseGAN: Sparse Generative Adversarial Network for Text Generation [article]

Liping Yuan, Jiehang Zeng, Xiaoqing Zheng
2021 arXiv   pre-print
Inspired by the principle of sparse coding, we propose a SparseGAN that generates semantic-interpretable, but sparse sentence representations as inputs to the discriminator.  ...  With such semantic-rich representations, we not only reduce unnecessary noises for efficient adversarial training, but also make the entire training process fully differentiable.  ...  Some examples of greedy algorithm include Matching Pursuit (MP), Orthogonal Matching Pursuit (OMP) [Tropp and Gilbert, 2007] , and Compressive Sampling Matching Pursuit (CoSAMP) [Needell and Tropp, 2009  ... 
arXiv:2103.11578v1 fatcat:nb5ejpmai5e2tfbz5fcpbtkrm4

Recovery of Sparse Translation-Invariant Signals With Continuous Basis Pursuit

Chaitanya Ekanadham, Daniel Tranchina, Eero P. Simoncelli
2011 IEEE Transactions on Signal Processing  
We consider the problem of decomposing a signal into a linear combination of features, each a continuously translated version of one of a small set of elementary features.  ...  The basis pursuit denoising (BP) method may be seen as a special case, in which the auxiliary interpolation functions are omitted, and we thus refer to our methodology as continuous basis pursuit (CBP)  ...  ACKNOWLEDGMENT The authors would like to thank Sinan Güntürk and Michael Orchard for helpful discussions in the early stages of this work.  ... 
doi:10.1109/tsp.2011.2160058 pmid:24352562 pmcid:PMC3860587 fatcat:glk66y2naraonpkfwopsv77obe

An artificial bee colony optimization based matching pursuit approach for ultrasonic echo estimation

Ai-Ling Qi, Guang-Ming Zhang, Ming Dong, Hong-Wei Ma, David M. Harvey
2018 Ultrasonics  
The performance of ABC-MP is tested using both simulated signals and real ultrasonic signals, and compared with matching pursuit.  ...  The optimal atoms are searched from a continuous parameter space over a tailored Gabor dictionary in ABC-MP instead of a discrete parameter space in matching pursuit.  ...  ACKNOWLEDGEMENTS This work has been supported by the National Natural Science Foundation of China (No. 61674121, No.51705418).  ... 
doi:10.1016/j.ultras.2018.03.002 pmid:29525226 fatcat:xm4o6xl6avevpaga3ht2qjnimy
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