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sparse representation-based correlation analysis of non-stationary spatiotemporal big data

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International Journal of Digital Earth 2016年第9期9卷 892-913页

作者： Weijing Song Peng Liu Lizhe Wang Institute of Remote Sensing and Digital Earth Chinese Academy of SciencesBeijingPeople’s Republic of China School of Computer Science China University of GeosciencesWuhanPeople’s Republic of China School of Computer and Communication Engineering University of Chinese Academy of SciencesBeijingPeople’s Republic of China

As the basic data of digital city and smart city research,Spatiotemporal series data contain rich geographic *** the accumulation of spatial time-series data,we are also encountering new challenges related to analyzing and mining the correlations among the *** the traditional methods of analysis also have their own suitable condition restrictions for the new features,we propose a new analytical framework based on sparse representation to describe the time,space,and spatial-time ***,before analyzing the correlation,we discuss sparse representation based on the K-singular value decomposition(K-SVD)algorithm to ensure that the sparse coefficients are in the same sparse *** then present new computing methods to calculate the time,spatial,and spatial-time correlation coefficients in the sparse domain;we then discuss the functions’***,we discuss change regulations for the gross domestic product(GDP),population,and Normalized Difference Vegetation Index(NDVI)spatial time-series data in China’s Jing-Jin-Ji region to confirm the effectiveness and adaptability of the new methods.

关键词： sparse representation correlation analysis Spatiotemporal data spatial data analysis

sparse representation and blind source separation of ill-posed mixtures

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Science in China(Series F) 2006年第5期49卷 639-652页

作者： HE Zhaoshui XIE Shengli FU Yu School of Electronics & Information Engineering South China University of Technology Guangzhou 510640 China

Bofill et al. discussed blind source separation （BSS） of sparse signals in the case of two sensors. However, as Bofill et al. pointed out, this method has some limitation. The potential function they introduced is lack of theoretical basis. Also the method could not be extended to solve the problem in the case of more than three sensors. In this paper, instead of the potential function method, a K-PCA method （combining K-clustering with PCA） is proposed. The new method is easy to be used in the case of more than three sensors. It is easy to be implemented and can provide accurate estimation of mixing matrix. Some criterion is given to check the effect of the mixing matrix A. Some simulations illustrate the availability and accuracy of the method we proposed.

关键词： ill-posed mixture blind source separation sparse representation PCA K-mean clustering.

Trajectory Estimation with Multi-range-rate System Based on sparse representation and Spline Model Optimization

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Chinese Journal of Aeronautics 2010年第1期23卷 84-90页

作者： Liu Jiying,Zhu Jubo,Xie Meihua Department of Mathematics and Systems Science,National University of Defense Technology,Changsha 410073,China Department of Mathematics and Systems Science National University of Defense Technology Changsha 410073 China

The classic state methods for trajectory estimation in boost phase with multi-range-rate system include method of point-by-point manner and that of spline-model-based manner. Both are deficient in terms of model-approximation accuracy and systematic error determination thus resulting in the estimation errors well beyond the requirements, especially, concerning the maneuvering trajectory. This article proposes a new high-precision estimation approach based on the residual error analysis. The residual error comprises three components, i. e. systematic error, model truncation error and random error. The approach realizes self-adaptive estimation of systematic errors in measurements following the theory of sparse representation of signals to minimize the low-frequency components of residual errors. By taking median- and high-frequency components as indexes, the spline model-approximation is improved by optimizing node sequence of the spline function and the weight selection for data fusion through iteration. Simulation has validated the performances of the proposed method.

关键词： tracking radar systematic errors truncation error sparse representation splines

Infrared small target detection using sparse representation

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Journal of Systems Engineering and Electronics 2011年第6期22卷 897-904页

作者： Jiajia Zhao ZhengyuanTang Jie Yang Erqi Liu Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200240 P. R. China China Aerospace Science and Industry Corporation Beijing 100074 P. R. China

sparse representation has recently been proved to be a powerful tool in image processing and object *** paper proposes a novel small target detection algorithm based on this *** modelling a small target as a linear combination of certain target samples and then solving a sparse 0-minimization problem,the proposed apporach successfully improves and optimizes the small target representation with ***,the sparsity concentration index（SCI） is creatively employed to evaluate the coefficients of each block representation and simpfy target *** the detection frame,target samples are firstly generated to constitute an over-complete dictionary matrix using Gaussian intensity model（GIM）,and then sparse model solvers are applied to finding sparse representation for each sub-image ***,SCI lexicographical evalution of the entire image incorparates with a simple threshold locate target *** effectiveness and robustness of the proposed algorithm are demonstrated by the exprimental results.

关键词： target detection sparse representation orthogonal matching pursuit(OMP).

Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure

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Science China(Information Sciences) 2018年第1期61卷 86-98页

作者： Xiao MA Fandong ZHANG Yuelong LI Jufu FENG School of Electronics Engineering and Computer Science Peking University School of Computer Science and Software Engineering Tianjin Polytechnic University Department of Computer Science University of York

The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches’ reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure(RSRC-ASP) is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals’ aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

关键词： face recognition sparse representation self-adaptive weighted aggregating spatial pyramid structure local robust strategies

Learning block-structured incoherent dictionaries for sparse representation

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Science China(Information Sciences) 2015年第10期58卷 79-93页

作者： ZHANG YongQin XIAO JinSheng LI ShuHong SHI CaiYun XIE GuoXi School of Information Science and Technology Northwest University School of Electronic Information Wuhan University College of Computer and Information Engineering Henan University of Economics and Law Shenzhen Key Laboratory for MRI Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences

Dictionary learning is still a challenging problem in signal and image processing. In this paper, we propose an efficient block-structured incoherent dictionary learning algorithm for sparse representations of image signals. The constrained minimization of dictionary learning is achieved by iteratively alternating between sparse coding and dictionary update. Without relying on any prior knowledge of the group structure for the input data, we develop a two-stage clustering method that identifies the underlying block structure of the dictionary under certain restricted constraints. The two-stage clustering method mainly consists of affinity propagation and agglomerative hierarchical clustering. To meet the conditions of both the upper bound and the lower bound of the mutual coherence of dictionary atoms, we introduce a regularization term for the objective function to adjust the block coherence of the overcomplete dictionary. The experiments on synthetic data and real images demonstrate that the proposed dictionary learning algorithm has lower representation error, higher visual quality and better reconstructed results than most of the state-of-the-art methods.

关键词： dictionary learning sparse representation sparse coding block sparsity mutual coherence

DME Interference mitigation for L-DACS1 based on system identification and sparse representation

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Chinese Journal of Aeronautics 2016年第6期29卷 1762-1773页

作者： Li Douzhe Wu Zhijun School of Electronic and Information Engineering Tianjin University School of Electronic and Information Engineering Civil Aviation University of China

L-band digital aeronautical communication system 1(L-DACS1) is a promising candidate data-link for future air-ground communication, but it is severely interfered by the pulse pairs(PPs) generated by distance measure equipment. A novel PP mitigation approach is proposed in this paper. Firstly, a deformed PP detection(DPPD) method that combines a filter bank, correlation detection, and rescanning is proposed to detect the deformed PPs(DPPs) which are caused by multiple filters in the receiver. Secondly, a finite impulse response(FIR) model is used to approximate the overall characteristic of filters, and then the waveform of DPP can be acquired by the original waveform of PP and the FIR model. Finally, sparse representation is used to estimate the position and amplitude of each DPP, and then reconstruct each DPP. The reconstructed DPPs will be subtracted from the contaminated signal to mitigate interference. Numerical experiments show that the bit error rate performance of our approach is about 5 dB better than that of recent works and is closer to interference-free environment.

关键词： DME interference L-DACS1 Least square approximations Proximal gradient algorithm sparse representation

DOA Estimation Based on sparse representation of the Fractional Lower Order Statistics in Impulsive Noise

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IEEE/CAA Journal of Automatica Sinica 2018年第4期5卷 860-868页

作者： Sen Li Rongxi He Bin Lin Fei Sun Department of Information Science and Technology Dalian Maritime UniversityDalian 116026China IEEE

This paper is mainly to deal with the problem of direction of arrival(DOA) estimations of multiple narrow-band sources impinging on a uniform linear array under impulsive noise environments. By modeling the impulsive noise as α-stable distribution, new methods which combine the sparse signal representation technique and fractional lower order statistics theory are proposed. In the new algorithms, the fractional lower order statistics vectors of the array output signal are sparsely represented on an overcomplete basis and the DOAs can be effectively estimated by searching the sparsest coefficients. To enhance the robustness performance of the proposed algorithms,the improved algorithms are advanced by eliminating the fractional lower order statistics of the noise from the fractional lower order statistics vector of the array output through a linear transformation. Simulation results have shown the effectiveness of the proposed methods for a wide range of highly impulsive environments.

关键词： α-stable distribution direction of arrival(DOA) fractional lower-order statistics impulsive noise sparse representation

A framework based on sparse representation model for time series prediction in smart city

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Frontiers of Computer Science 2021年第1期15卷 99-111页

作者： Zhiyong YU Xiangping ZHENG Fangwan HUANG Wenzhong GUO Lin SUN Zhiwen YU College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350108China Hangzhou Key Laboratory for IoT Technology&Application Zhejiang University City CollegeHangzhou 310015China School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China

Smart city driven by Big Data and Internet of Things(loT)has become a most promising trend of the *** one important function of smart city,event alert based on time series prediction is faced with the challenge of how to extract and represent discriminative features of sensing knowledge from the massive sequential data generated by IoT *** this paper,a framework based on sparse representa-tion model(SRM)for time series prediction is proposed as an efficient approach to tackle this *** dividing the over-complete dictionary into upper and lower parts,the main idea of SRM is to obtain the sparse representation of time series based on the upper part firstly,and then realize the prediction of future values based on the lower *** choice of different dictionaries has a significant impact on the performance of *** paper focuses on the study of dictionary construction strategy and summarizes eight variants of *** results demonstrate that SRM can deal with different types of time series prediction flexibly and effectively.

关键词： sparse representation smart city time series prediction dictionary construction