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检索条件"主题词=Pursuit Algorithms"

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Sparse recovery properties of statistical RIP matrices

Sparse recovery properties of statistical RIP matrices

引用

Annual Allerton Conference on Communication, Control, and Computing

作者： Arya Mazumdar Alexander Barg Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge MA USA Department of ECE and Institute for Systems Research University of Maryland College Park MD USA

Compressive sampling is a technique of recovering sparse N-dimensional signals from low-dimensional sketches, i.e., their linear images in R m , m ≪ N. The main question associated with this technique is construction of linear operators that allow faithful recovery of the signal from its sketch. The most frequently used sufficient condition for robust recovery is the near-isometry property of the operator when restricted to k-sparse signals. In this paper we study performance of standard sparse recovery algorithms in the situation when the sampling matrices satisfy statistical isometry properties. Namely, we show it is possible to recover a sparse signal from its sketch with high probability using the basis pursuit algorithm. Moreover, the same statistical isometry conditions are sufficient for robust model selection with the Lasso algorithm. Finally we show that matrices with the needed properties can be constructed from binary error-correcting codes.

关键词： Vectors Sparse matrices Compressed sensing Integrated circuits pursuit algorithms Robustness Probability

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ECG COMPRESSED SENSING BASED ON CLASSIFICATION IN COMPRESSED SPACE AND SPECIFIED DICTIONARIES

ECG COMPRESSED SENSING BASED ON CLASSIFICATION IN COMPRESSED...

引用

European Signal Processing Conference

作者： Catalina Monica Fira Liviu Goras Constantin Barabasa Nicolae Cleju Institute of Computer Science Romanian Academy "Gheorghe Asachi" Technical University of Iasi

An electrocardiographic signal (ECG) compressed sensing (CS) method, its reconstruction using specific dictionaries of cardiac pathologies and method evaluation testing using classical measures as well as by classification error of the reconstructed patterns based on the K-Nearest Neighbour classifier (KNN) are presented. For compressed sensing, a random matrix with standard normal distribution was used, followed by a classification of compressed signals in one of eight possible pathological classes. For each class a specific dictionary was created, and the signals were reconstructed using the Basis pursuit algorithm according to the result of the classification.

关键词： Taxonomy COMPRESSED Electrocardiography electrochemical grinding compressed signal Standard Normal distribution Dictionaries as Topic Compressed sensing signals pursuit algorithms

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GRADIENT POLYTOPE FACES pursuit FOR LARGE SCALE SPARSE RECOVERY PROBLEMS

GRADIENT POLYTOPE FACES PURSUIT FOR LARGE SCALE SPARSE RECOV...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Aris Gretsistas Ivan Damnjanovic Mark D. Plumbley Queen Mary University of London Centre for Digital Music Mile End Road E1 4NS London UK

ISBN: (纸本)9781424442959

Polytope Faces pursuit is a greedy algorithm that performs Basis pursuit with similar order complexity to Orthogonal Matching pursuit. The algorithm adds one basis vector at a time and adopts a path-following approach based on the geometry of the polar polytope associated with the dual Linear Program. Its initial implementation uses the method of Cholesky factorization to update the solution vector at each step, which can be computationally expensive for solving large scale problems as it requires the succesive storage of large matrices. In this paper, we present a different approach using directional updates to estimate the solution vector at each time. The proposed method uses the gradient descent method, reducing the memory requirements and computational complexity. We demonstrate the application of this Gradient Polytope Faces pursuit algorithm to a source separation problem.

关键词： Sparse representation greedy algorithm Polytope Faces pursuit Gradient descent Source Separation Source separation greedy algorithm sparse representation Polytopes pursuit algorithms memory requirements Scale formation Cholesky factorisation descent method LARGE gene

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A Water Quality Evaluation Method of Projection pursuit Regression Based on Ant Colony Algorithm in Nanning Urban River

A Water Quality Evaluation Method of Projection Pursuit Regr...

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International Conference on Bioinformatics and Biomedical Engineering (ICBBE)

作者： Chong Fang Liang Wei Weijun Huang Feipeng Xiao Dept. of Hydraulic Eng. Guangxi Technol. Coll. of Water Conservancy & Electr. Power Nanning China Coll. of Civil & Aitectural Eng. Guangxi Univ. Nanning China

Pollution problem is getting worse in China's urban rivers at present. This paper, after analyzing the factors affecting urban river water quality and choosing such six principal evaluation factors as BOD 5 , CODcr, petroleum, volatile phenol, NH 3 -N and total phosphorus, establishes a Projection pursuit analysis model for evaluating water quality in urban rivers, optimizes the evaluation model with the help of Ant Colony Algorithm, and applies the model in the evaluation and ordering of the water quality in ten urban rivers in Nanning. The research shows that Projection pursuit Regression Analysis in water quality evaluation helps avoid the errors caused by the subjective reasons as manifested in traditional evaluation methods, and, as the evaluation results are accurate and the method is simple, it can serve as a new way to evaluate the water quality in China's urban rivers.

关键词： pursuit algorithms Rivers Water pollution Water conservation Ant colony optimization Power engineering and energy Educational institutions Urban pollution Algorithm design and analysis Regression analysis

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Matching pursuit: Evaluation and implementatio for LTE channel estimation

Matching pursuit: Evaluation and implementatio for LTE chann...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： P. Maechler P. Greisen N. Felber A. Burg Integrated Systems Laboratory ETH Zurich Zurich Switzerland

The emerging research field of compressed sensing (CS) promises better signal reconstruction out of fewer measurements if a sparse representation of the signal exists. Since wireless broadband channels often exhibit a sparse impulse response, CS reconstruction algorithms were proposed for channel estimation. In this paper, a hardware architecture for channel estimation using the matching pursuit algorithm is presented. The reference design targets the 3GPP LTE standard with a channel bandwidth of up to 20 MHz. Achievable performance gains over least squares channel estimation are illustrated by means of simulations. The costs in terms of chip area and reconstruction time for 180 nm CMOS technology are presented together with an analysis of the tradeoff between hardware complexity and reconstruction performance.

关键词： Matching pursuit algorithms Channel estimation Hardware CMOS technology Compressed sensing Signal reconstruction Wireless sensor networks Reconstruction algorithms pursuit algorithms Bandwidth

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ACO-based Projection pursuit clustering algorithm

ACO-based Projection Pursuit clustering algorithm

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International Asia Conference on Informatics in Control, Automation and Robotics, CAR

作者： Yancang Li Zhao Lina Shujing Zhou College of Civil Engineering Hebei University of Engineering Handan China College of Civil Engineering Hebei University of Engineering Handan China

In order to find a more effective method of solving the problem of subjectivity and difficulty to deal with the high-dimension data in the clustering, a new method---an improved PP (Projection pursuit) based on Ant Colony Optimization algorithm (ACO) was introduced. The ant colony optimization algorithm has the strong global optimization ability and the PP method is a powerful technique for extracting statistically significant features from high-dimension data for automatic target detection and classification. The ant colony optimization algorithm was employed to optimize the function of the projected indexes in the PP. Application results show that the method can complete the selection more objectivity and rationality with objective weight, high resolving power, and stable result.

关键词： Clustering algorithms pursuit algorithms Ant colony optimization Robotics and automation Optimization methods Fuzzy sets Artificial neural networks Asia Informatics Automatic control

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Model of projection pursuit drought evaluation based on improved artificial fish swarm algorithm of Sanjiang Plain

Model of projection pursuit drought evaluation based on impr...

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International Conference on Mechanic Automation and Control Engineering (MACE)

作者： Ding Hong Liu Dong Li Tao School of Water Conservancy & Civil Engineering Northeast Agricultural University Harbin Heilongjiang China School of Water Conservancy & Civil Engineering Postdoctoral Scientific Research Mobile Station of Agriculture and Forestry Economic Management Northeast Agricultural University Harbin Heilongjiang China

ISBN: (纸本)9781424477371

The model of projection pursuit drought evaluation based on artificial fish swarm algorithm was proposed to optimize the function and seek the optimum projection for solving the problems such as discrete evaluation grade and low resolved results obtained by using other methods. In order to avoid local optimization and improve global search ability and convergence speed of the algorithm, the adaptive artificial fish step and crowded degree factor were applied to optimize artificial fish swarm algorithm. The departure ratio of rain-fall, Z index and homogenization of rain-temperature were selected to establish evaluation model of Sanjiang Plain based on above methods. The results showed that model effectively avoided the incompatibility of single index. It was feasible to evaluate actual drought situation, and provide a new way for drought evaluation.

关键词： Marine animals pursuit algorithms Convergence Water conservation Civil engineering Electronic mail National security Environmental economics Optimization methods Production

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Non-linear Matching pursuits and its apllication in bone mineral measurement

Non-linear Matching Pursuits and its apllication in bone min...

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IEEE Instrumentation and Measurement Technology Conference

作者： Xiangli Shen Yi Shen Qiang Wang Limei Shen Naizhang Feng Jing Jin Department of Control Science and Engineering Harbin Institute of Technology Harbin China

Weakening of the bones because of the progressive loss of bone mass is a potentially serious side effect of extended space flight and it is a disease that cannot make complete recovery after landing. The measurement, prevention and treatment of bone loss are indispensable for long-term space flight. Quantitative ultrasound (QUS) measurement is the only densitometry technologies of bone mineral density (BMD) that is non-radioactive and can be used in space flights. Speed of Sound (SOS) is a significant parameter in ultrasonic BMD measurement. To get precise SOS value, the thickness of bone and the time that sound passes through must be accurate. Frequently used methods to gauge delays, such as maximum value point, zero-crossing detector, and threshold detector, are not so precise and can easily be disturbed by noise. This article analyses the composing of the receiving signals, and proposes a method to extract valid signals and calculate the time difference using Matching pursuits algorithm. Besides, to eliminate the influence of saturation property widely encountered in ultrasound receiving circuits, a non-linear Matching pursuits algorithm is introduced and trends to be effective.

关键词： Matching pursuit algorithms Minerals Ultrasonic variables measurement Space technology Extraterrestrial measurements Ultrasonic imaging Detectors pursuit algorithms Bone diseases Loss measurement

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Performance tradeoffs for exact support recovery of sparse signals

Performance tradeoffs for exact support recovery of sparse s...

引用

IEEE International Symposium on Information Theory

作者： Yuzhe Jin Young-Han Kim Bhaskar D. Rao Department of Electrical and Computer Engineering University of California San Diego La Jolla CA USA

ISBN: (纸本)9781424478903

We study the tradeoffs between the number of measurements, the signal sparsity level, and the measurement noise level for exact support recovery of sparse signals via random noisy measurements. By drawing analogy between exact support recovery and communication over the Gaussian multiple access channel, and exploiting mathematical tools developed for the latter problem, we derive sharp asymptotic sufficient and necessary conditions for exact support recovery. Specifically, when the number of nonzero entries is held fixed, the exact asymptotics on the number of measurements for support recovery is developed. When the number of nonzero entries increases in certain manners, we obtain sufficient conditions tighter than existing results. The proposed information theoretic framework for analyzing the performance of support recovery is further demonstrated to be capable of dealing with a variety of sparse signal recovery models.

关键词： Noise measurement Matching pursuit algorithms Maximum likelihood decoding Electric variables measurement Performance analysis pursuit algorithms Noise level Signal analysis Drives Engineering drawings

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On Growth and Formlets: Sparse Multi-Scale Coding of Planar Shape

On Growth and Formlets: Sparse Multi-Scale Coding of Planar ...

引用

IEEE Conference on Computer Vision and Pattern Recognition

作者： Timothy D. Oleskiw James H. Elder Gabriel Peyre York University Universite Paris-Dauphine

ISBN: (纸本)9781424469840;9781424469857

This paper presents a sparse representation of 2D planar shape through the composition of warping functions, termed formlets, localized in scale and space. Each formlet subjects the 2D space in which the shape is embedded to a localized isotropic radial deformation. By constraining these localized warping transformations to be diffeomor-phisms, the topology of shape is preserved, and the set of simple closed curves is closed under any sequence of these warpings. A generative model based on a composition of formlets applied to an embryonic shape, e.g., an ellipse, has the advantage of synthesizing only those shapes that could correspond to the boundaries of physical objects. To compute the set of formlets that represent a given boundary, we demonstrate a greedy coarse-to-fine formlet pursuit algorithm that serves as a non-commutative generalization of matching pursuit for sparse approximations. We evaluate our method by pursuing partially occluded shapes, comparing performance against a contour-based sparse shape coding framework.

关键词： sparse representation Warping pursuit algorithms Shape Planar matching pursuits multi-scale Ellipse addendum modification shape coding

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