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Orthogonal Matching Pursuit with correction
2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), 2016Orthogonal Matching Pursuit (OMP) is the most popular greedy algorithm that has been developed to find a sparse solution vector to an under-determined linear system of equations. OMP follows the projection procedure to identify the indices of the support of the sparse solution vector.
Nasser Mourad +2 more
openaire +1 more source
IEEE Transactions on Industrial Informatics, 2020
To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper ...
Yi Qin +4 more
semanticscholar +1 more source
To detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-dimensional K-SVD algorithm is explored in this paper ...
Yi Qin +4 more
semanticscholar +1 more source
Quaternion Generalized Orthogonal Matching Pursuit
International Conference on Wavelet Analysis and Pattern RecognitionQuaternion Orthogonal Matching Pursuit (QOMP) pioneers the application of quaternions in color image processing, garnering widespread attention for its superior performance.
Feng He, Cui-Ming Zou
semanticscholar +1 more source
, 2021
In the fault diagnosis of bearings, the high flexibility of the asymmetric Gaussian chirplet model enables the adapted dictionary-free orthogonal matching pursuit to manifest good performance. Since this method does not rely on predetermined dictionaries,
Lingli Cui +3 more
semanticscholar +1 more source
In the fault diagnosis of bearings, the high flexibility of the asymmetric Gaussian chirplet model enables the adapted dictionary-free orthogonal matching pursuit to manifest good performance. Since this method does not rely on predetermined dictionaries,
Lingli Cui +3 more
semanticscholar +1 more source
Stagewise Arithmetic Orthogonal Matching Pursuit
International Journal of Wireless Information Networks, 2018In order to improve the problems that stagewise weak orthogonal matching pursuit (SWOMP) has low reconstruction accuracy and imprecise choice of indexs selecting, an effective algorithm called stagewise arithmetic orthogonal matching pursuit (SAOMP) was proposed.
Yingying Zhang, Guiling Sun
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IEEE Transactions on Circuits and Systems Part 1: Regular Papers
Compressed sensing (CS) theory realizes sparse signal sampling at a sub-Nyquist frequency to reduce the high computational cost of signal processing systems.
Sujuan Liu, Jiajun Ma, Chengkai Cui
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Compressed sensing (CS) theory realizes sparse signal sampling at a sub-Nyquist frequency to reduce the high computational cost of signal processing systems.
Sujuan Liu, Jiajun Ma, Chengkai Cui
semanticscholar +1 more source
2021 International Conference on Wireless Communications and Smart Grid (ICWCSG), 2021
In recent years, sparse representation technology has made outstanding contributions in signal processing, image processing, target recognition, blind source separation, etc.
Junshuo Dong, Lingda Wu
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In recent years, sparse representation technology has made outstanding contributions in signal processing, image processing, target recognition, blind source separation, etc.
Junshuo Dong, Lingda Wu
semanticscholar +1 more source
A secondary selection-based orthogonal matching pursuit method for rolling element bearing diagnosis
, 2021Sparse representation based on the matching pursuit (MP) algorithm is an effective method for fault feature extraction involving rolling element bearings. However, in the sparse decomposition stage, the MP algorithm is extremely susceptible to both noise
Yongjian Li +3 more
semanticscholar +1 more source
Look ahead orthogonal matching pursuit
2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011For compressive sensing, we endeavor to improve the recovery performance of the existing orthogonal matching pursuit (OMP) algorithm. To achieve a better estimate of the underlying support set progressively through iterations, we use a look ahead strategy.
Saikat Chatterjee +2 more
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From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
arXiv.orgMotivated by the hypothesis that neural network representations encode abstract, interpretable features as linearly accessible, approximately orthogonal directions, sparse autoencoders (SAEs) have become a popular tool in interpretability.
Valérie Costa +4 more
semanticscholar +1 more source