检索结果-内蒙古大学图书馆

Bayesian track-before-detect algorithm with target amplitude fluctuation based on expectation-maximisation estimation

引用

IET Radar, Sonar and Navigation 2012年第8期6卷 719-728页

作者： Xia, S.Z. Liu, H.W. National Lab of Radar Signal Processing Xidian University 710071 Shaanxi China

Bayesian track-before-detect is an efficient approach to detect low observable targets. Before implementing Bayesian track-before-detect, one needs to exactly ascertain the target motion model and the measurement model. When the target return amplitude fluctuates, the target return amplitude of the measurement model is not known a priori. In the scenario, standard Bayesian track-before-detect algorithms such as particle filters, which assume perfect knowledge of the model parameters, cannot work well. In this study, the authors propose an expectation-maximisation (EM) algorithm for Bayesian track-before-detect with target amplitude fluctuation, in which the fluctuation models are incorporated into the likelihood function, and the average target return amplitude is estimated by the EM algorithm. The simulation results show that the average target return amplitude can be estimated by the EM algorithm, which is helpful in improving the performance of detection and tracking. Therefore it is feasible to apply the EM algorithm to Bayesian track-before-detect with target amplitude fluctuation. © 2012 The Institution of Engineering and Technology.

关键词： expectation-maximization algorithms BAYESIAN analysis PARAMETER estimation MATHEMATICAL models SIMULATION methods & models AMPLITUDE modulation

来源：评论

学校读者我要写书评

暂无评论

SMEM algorithm is not fully compatible with maximum-likelihood framework

引用

NEURAL COMPUTATION 2002年第6期14卷 1261-1266页

作者： Minagawa, A Tagawa, N Tanaka, T Tokyo Metropolitan Univ Grad Sch Engn Tokyo 1920397 Japan

The expectation-maximization (EM) algorithm with split-and-merge operations (SMEM algorithm) proposed by Ueda, Nakano, Ghahramani, and Hinton (2000) is a nonlocal searching method, applicable to mixture models, for relaxing the local optimum property of the EM algorithm. In this article, we point out that the SMEM algorithm uses the acceptance-rejection evaluation method, which may pick up a distribution with smaller likelihood, and demonstrate that an increase in likelihood can then be guaranteed only by comparing log likelihoods.

关键词： expectation-maximization algorithms algorithms

来源：评论

学校读者我要写书评

暂无评论

INTENSITY ESTIMATION FROM SHOT-NOISE DATA

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 1995年第6期43卷 1527-1531页

作者： SEQUEIRA, RE GUBNER, JA Department of Electrical and Computer Engineering University of Wisconsin

The estimation of the intensity function of a Poisson-driven shot-noise process is addressed using a regularization technique, where the data is modeled as a signal term plus a signal-dependent noise term. A new data-based method for selecting a pair of regularization parameters is presented and compared with the minimum unbiased risk method. The detail in the intensity function can be recovered by both methods, but the new method does a better job at suppressing spurious oscillations.

关键词： Signal processing Statistics Nonlinear filters Integral equations Random variables expectation-maximization algorithms Image reconstruction Image restoration Military computing Smoothing methods

来源：评论

学校读者我要写书评

暂无评论

REDUCING NEGATIVITY ARTIFACTS IN EMISSION TOMOGRAPHY - POST-PROCESSING FILTERED BACKPROJECTION SOLUTIONS

引用

IEEE TRANSACTIONS ON MEDICAL IMAGING 1993年第4期12卷 653-663页

作者： OSULLIVAN, F PAWITAN, Y HAYNOR, D UNIV WASHINGTON DEPT BIOSTATSEATTLEWA 98195 UNIV COLL DUBLIN DEPT STATBELFIELD 4DUBLINIRELAND UNIV WASHINGTON DEPT RADIOLSEATTLEWA 98195

The problem of negative artifacts in emission tomography reconstructions computed by filtered backprojection (FBP) is of practical concern particularly in low count studies. Statistical reconstruction methods based on maximum likelihood (ML) are automatically constrained to be non-negative but their excessive computational overhead (orders of magnitude greater than FBP) has limited their use in operational settings. Motivated by the statistical character of the negativity artifact, we develop a simple post-processing technique that iteratively adjusts negative values by cancellation with positive values in a surrounding local neighborhood. The compute time of this approach is roughly equivalent to two applications of FBP. The approach was evaluated by numerical simulation in one- and two-dimensional settings. In two dimensions, the source distributions included the Hoffman, the Shepp and Vardi, and a digitized version of the Jaszczak cold spheres phantoms. Our studies compared smoothed versions of FBP, the post-processed FBP, and ML implemented by the expectation-maximization algorithm. The root mean square (RMS) error between the true and estimated source distribution was used to evaluate performance;in two dimensions, additional region-of-interest-based measures of reconstruction accuracy were also employed. In making comparisons between the different methods, the amount of smoothing applied to each reconstruction method was adapted to minimize the RMS error-this was found to be critical. After adjusting for this effect, the average RMS error for FBP was typically between 13% and 20% higher than ML;the modified FPB method was between 2% and 5% greater than ML. Similar results were found for the region-of-interest error. These experiments suggest that it may be possible to achieve much of the gain associated with ML reconstruction in emission tomography by simpler computational means.

关键词： Tomography Reconstruction algorithms Image reconstruction Statistics Bayesian methods Maximum likelihood estimation Numerical simulation Imaging phantoms expectation-maximization algorithms Root mean square

来源：评论

学校读者我要写书评

暂无评论

Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

引用

INTERNATIONAL JOURNAL OF REMOTE SENSING 2017年第13期38卷 3886-3905页

作者： Peng, Daifeng Zhang, Yongjun Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Hubei Peoples R China

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the expectation-maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.

关键词： REMOTE-sensing images HIGH resolution imaging expectation-maximization algorithms OBJECT-oriented methods (Computer science) REMOTE sensing

来源：评论

学校读者我要写书评

暂无评论

A Framework for Clustered and Skewed Sparse Signal Recovery

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第15期66卷 3972-3986页

作者： Wang, Sheng Rahnavard, Nazanin Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

A novel framework, clustered-skew normal mixture-belief propagation, is developed to solve the reconstruction of undersampled clustered signals, where the magnitudes of signal coefficients in each cluster are distributed asymmetrically w.r.t the cluster mean. To address the skewness feature, a finite skew-normal density mixture is utilized to model the prior distribution, where the marginal posterior of the signal is inferred by an efficient approximate message-passing-based algorithm. An expectation-maximization-based algorithm is developed to estimate the mixture density. The clustered property is then modeled by the Potts model, and a loopy belief propagation algorithm is designed to promote the spatial feature. Experimental results show that our technique is highly effective and efficient in exploiting both the clustered feature and asymmetrical feature of the signals and outperforms many sophisticated techniques.

关键词： Compressed sensing asymmetrical signal approximate message passing expectation-maximization algorithms

来源：评论

学校读者我要写书评

暂无评论

Penalized probabilistic clustering

引用

NEURAL COMPUTATION 2007年第6期19卷 1528-1567页

作者： Lu, Zhengdong Leen, Todd K. Oregan Hlth & Sci Inst Dept Comp Sci & Engn OGI Sch Sci & Engn Beaverton OR 97006 USA

While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of out-of-sample data in a manner consistent with the prior knowledge expressed in the training set. Our starting point is probabilistic clustering based on gaussian mixture models (GMM) of the data distribution. We express clustering preferences in a prior distribution over assignments of data points to clusters. This prior penalizes cluster assignments according to the degree with which they violate the preferences. The model parameters are fit with the expectation-maximization (EM) algorithm. Our model provides a flexible framework that encompasses several other semisupervised clustering models as its special cases. Experiments on artificial and real-world problems show that our model can consistently improve clustering results when pairwise relations are incorporated. The experiments also demonstrate the superiority of our model to other semisupervised clustering methods on handling noisy pairwise relations.

关键词： expectation-maximization algorithms algorithms PROBABILISTIC number theory MATHEMATICAL models DATA analysis

来源：评论

学校读者我要写书评

暂无评论

EP-GIG Priors and Applications in Bayesian Sparse Learning

引用

JOURNAL OF MACHINE LEARNING RESEARCH 2012年第6期13卷 2031-2061页

作者： Zhang, Zhihua Wang, Shusen Liu, Dehua Jordan, Michael I. Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Zhejiang Peoples R China Univ Calif Berkeley Div Comp Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA

In this paper we propose a novel framework for the construction of sparsity-inducing priors. In particular, we define such priors as a mixture of exponential power distributions with a generalized inverse Gaussian density (EP-GIG). EP-GIG is a variant of generalized hyperbolic distributions, and the special cases include Gaussian scale mixtures and Laplace scale mixtures. Furthermore, Laplace scale mixtures can subserve a Bayesian framework for sparse learning with nonconvex penalization. The densities of EP-GIG can be explicitly expressed. Moreover, the corresponding posterior distribution also follows a generalized inverse Gaussian distribution. We exploit these properties to develop EM algorithms for sparse empirical Bayesian learning. We also show that these algorithms bear an interesting resemblance to iteratively reweighted l(2) or l(1) methods. Finally, we present two extensions for grouped variable selection and logistic regression.

关键词： sparsity priors scale mixtures of exponential power distributions generalized inverse Gaussian distributions expectation-maximization algorithms iteratively reweighted minimization methods

来源：评论

学校读者我要写书评

暂无评论

Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula)

引用

IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第3期69卷 1824-1852页

作者： Gerbelot, Cedric Abbara, Alia Krzakala, Florent Univ PSL Sorbonne Univ Lab Phys Ecole normale Super CNRS F-75005 Ens Paris France NYU Courant Inst Math Sci New York NY 10012 USA Lab Phys Ecole normalesu perieure EPFL F-75005 Ens Paris France Lab Computat Biol & Theoret Biophys EPFL CH-1015 Lausanne Switzerland IdePh Lab EPFL CH-1015 Lausanne Switzerland

There has been a recent surge of interest in the study of asymptotic reconstruction performance in various cases of generalized linear estimation problems in the teacher-student setting, especially for the case of i.i.d standard normal matrices. Here, we go beyond these matrices, and prove an analytical formula for the reconstruction performance of convex generalized linear models with rotationally-invariant data matrices with arbitrary bounded spectrum, rigorously confirming, under suitable assumptions, a conjecture originally derived using the replica method from statistical physics. The proof is achieved by leveraging on message passing algorithms and the statistical properties of their iterates, allowing to characterize the asymptotic empirical distribution of the estimator. For sufficiently strongly convex problems, we show that the two-layer vector approximate message passing algorithm (2-MLVAMP) converges, where the convergence analysis is done by checking the stability of an equivalent dynamical system, which gives the result for such problems. We then show that, under a concentration assumption, an analytical continuation may be carried out to extend the result to convex (non-strongly) problems. We illustrate our claim with numerical examples on mainstream learning methods such as sparse logistic regression and linear support vector classifiers, showing excellent agreement between moderate size simulation and the asymptotic prediction.

关键词： Heuristic algorithms Approximation algorithms Estimation Convergence Signal processing algorithms Mathematical models Machine learning Parametric statistics estimation error message passing expectation-maximization algorithms optimization convergence convex functions linear matrix inequalities

来源：评论

学校读者我要写书评

暂无评论

Robust deterministic annealing based EM algorithm

引用

ELECTRONICS LETTERS 2012年第5期48卷 289-U1592页

作者： Wang, B. Wan, F. Mak, P. -U. Mak, P. -I. Vai, M. -I. Univ Macau Dept Elect & Comp Engn Taipa Macau Peoples R China McGill Univ Sch Comp Sci Montreal PQ H3A 2A7 Canada

A deterministic annealing (DA)-based expectation-maximisation (EM) algorithm is proposed for robust learning of Gaussian mixture models. By combing the DA approach, trimmed likelihood function and Bayesian information criterion (BIC), the proposed algorithm can simultaneously perform model selection and outlier detection, and mitigate the problems of local optima and boundary of parameter space with the conventional EM algorithm. Experiments demonstrate that the proposed algorithm can determine the number of components correctly even though the data are contaminated by outliers.

关键词： Bayesian analysis Simulated annealing (Mathematics) expectation-maximization algorithms Gaussian processes Maximum likelihood statistics Outliers (Statistics)

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：