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Ways to sparse representation:An overview

Science in China(Series F) 2009年第4期52卷 695-703页

作者： YANG JingYu PENG YiGang XU WenLi DAI QiongHai Department of Automation Tsinghua University Beijing 100084 China

Many algorithms have been proposed to find sparse representations over redundant dictionaries or transforms. This paper gives an overview of these algorithms by classifying them into three categories： greedy pursuit algorithms, lp norm regularization based algorithms, and iterative shrinkage algorithms. We summarize their pros and cons as well as their connections. Based on recent evidence, we conclude that the algorithms of the three categories share the same root： lp norm regularized inverse problem. Finally, several topics that deserve further investigation are also discussed.

关键词： sparse representation redundant dictionary redundant transform nonlinear approximation matching pursuit basis pursuit iterativeshrinkage

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Sparse Decomposition Algorithm Using Immune Matching Pursuit 11th

Sparse Decomposition Algorithm Using Immune Matching Pursuit

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2012 IEEE 11th International Conference on Signal Processing(ICSP 2012)

作者： Yan Zhou Heming Zhao Tao Liu School of Electronics and Information Engineering Soochow University Department of Electronics and Information Engineering Soochow Vocational University

ISBN: (纸本)9781467321969

The optimal sparse expression for a signal is critical to the application of compressed sensing theory. Since the standard matching pursuit uses greedy strategy to approximate the objective function, which is aim to search for the linear combination of the optimal atoms in a redundant dictionary. It is inevitable to cause enormous calculation quantity, to overcome this defect, a scheme based on immune matching pursuit algorithm is proposed in this paper which is suitable for speech signal sparse decomposition. In this matching pursuit algorithm, each matching process has been optimized by the immune algorithm with the characteristics of local fast convergence and global optimal. As a result, according to this algorithm, the speed of sparse decomposition and the quality of reconstructed signal all have been effectively improved, and the storage space requirement for redundant dictionary also has been degraded. Simulation results show that, the matching pursuit computational cost can be obviously reduced by using this algorithm, Moreover, the performance of this algorithm is stable, and it also has high signal decomposition accuracy. In a word, the efficiency of sparse decomposition can be significantly improved by combining the immune evolution mechanism and matching pursuit algorithm.

关键词： Matching Pursuit Immune Algorithm Sparse decomposition redundant dictionary Reconstructed signal

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Power-line interference suppression of MT data based on frequency domain sparse decomposition

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Journal of Central South University 2018年第9期25卷 2150-2163页

作者： TANG Jing-tian LI Guang ZHOU Cong LI Jin LIU Xiao-qiong ZHU Hui-jie School of Geosciences and Info-Physics Central South UniversityChangsha 410083China Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring Ministry of Education(Central South University)Changsha 410083China College of Information Science and Engineering Hunan Normal UniversityChangsha 410081China The First Engineering Scientific Research Institute of General Armaments Department Wuxi 214035China

Power-line interference is one of the most common noises in magnetotelluric(MT)*** usually causes distortion at the fundamental frequency and its odd harmonics,and may also affect other frequency *** trap circuits are designed to suppress such noise in most of the modern acquisition devices,strong interferences are still found in MT data,and the power-line interference will fluctuate with the changing of load *** fixed trap circuits often fail to deal with *** paper proposes an alternative scheme for power-line interference removal based on frequency-domain sparse ***,the fast Fourier transform of the acquired MT signal is ***,a redundant dictionary is designed to match with the power-line interference which is insensitive to the useful ***-line interference is separated by using the dictionary and a signal reconstruction algorithm of compressive sensing called improved orthogonal matching pursuit(IOMP).Finally,the frequency domain data are switched back to the time domain by the inverse fast Fourier *** experiments and real data examples from Lu-Zong ore district illustrate that this scheme can effectively suppress the power-line interference and significantly improve data *** with time domain sparse decomposition,this scheme takes less time consumption and acquires better results.

关键词： sparse representation magnetotelluric signal processing power-line noise improved orthogonal matching pursuit redundant dictionary

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