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Dynamic Orthogonal Matching Pursuit for Sparse Data Reconstruction
IEEE Open Journal of Signal Processing, 2023 The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for sparse data reconstruction or approximation. It acts as a driving force for the development of several other greedy methods for sparse data reconstruction, and it also plays a ...
Yun-Bin Zhao, Zhi-Quan Luo
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Online Orthogonal Matching Pursuit
CoRR, 2020 Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the sample complexity, with little or no consideration of the computation resources required.
El Mehdi Saad +2 more
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Orthogonal Matching Pursuit for Text Classification [PDF]
Proceedings of the 2018 EMNLP Workshop W-NUT: The 4th Workshop on Noisy User-generated Text, 2018 In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a
Konstantinos Skianis +2 more
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Electrical Faults Signals Restoring Based on Compressed Sensing Techniques
Energies, 2020 This research focuses on restoring signals caused by power failures in transmission lines using the basis pursuit, matching pursuit, and orthogonal matching pursuit sensing techniques.
Milton Ruiz, Iván Montalvo
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Perturbation Analysis of Orthogonal Matching Pursuit [PDF]
IEEE Transactions on Signal Processing, 2013 29 ...
Jie Ding, Laming Chen, Yuantao Gu
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Journal of Low Frequency Noise, Vibration and Active Control, 2021 This paper proposes a direction of arrival estimation based on sparse signal reconstruction in the presence of alpha noise by the off-grid orthogonal matching pursuit algorithm.
LongKai Liang +3 more
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Temperature Field Reconstruction Method for Acoustic Tomography Based on Multi-Dictionary Learning
Sensors, 2022 A reconstruction algorithm is proposed, based on multi-dictionary learning (MDL), to improve the reconstruction quality of acoustic tomography for complex temperature fields.
Yuankun Wei, Hua Yan, Yinggang Zhou
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Efficient Distributed Multi-Task Schemes for mmWave MIMO Channel Estimation
IEEE Access, 2022 In this study, the problem of sparse channel estimation is investigated with the employment of a fully distributed approach. We exploit the spatially joint sparsity structure of the involved channels to formulate the channel estimation problem in the ...
Maria Trigka +2 more
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Structured Bayesian Orthogonal Matching Pursuit [PDF]
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012 Taking advantage of the structures inherent in many sparse decompositions constitutes a promising research axis. In this paper, we address this problem from a Bayesian point of view. We exploit a Boltzmann machine, allowing to take a large variety of structures into account, and focus on the resolution of a joint maximum a posteriori problem.
Drémeau, Angélique +2 more
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A Low-complexity Sparse Channel Estimation Algorithm [PDF]
Jisuanji gongcheng, 2016 Traditional Compressed Sensing(CS)based channel estimation methods are difficult to implement due to their high computational complexity.To solve this problem,Generalized Orthogonal Matching Pursuit(GOMP)algorithm is used for channel estimation,which ...
FAN Xinyue,SU Yantao,ZHOU Fei
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