检索结果-内蒙古大学图书馆

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pedro A. Forero Alfonso Cano Georgios B. Giannakis Department of ECE University of Minnesota Minneapolis MN USA

The present paper develops a decentralized expectation-maximization (EM) algorithm to estimate the parameters of a mixture density model for use in distributed learning tasks performed with data collected at spatially deployed wireless sensors. The E-step in the novel iterative scheme relies on local information available to individual sensors, while during the M-step sensors exchange information only with their one- hop neighbors to reach consensus and eventually percolate the global information needed to estimate the wanted parameters across the wireless sensor network (WSN). Analysis and simulations demonstrate that the resultant consensus-based distributed EM (CB-DEM) algorithm matches well the resource- limited characteristics of WSNs and compares favorably with existing alternatives because it has wider applicability and remains resilient to inter-sensor communication noise.

关键词： expectation-maximization algorithms Wireless sensor networks Parameter estimation Sensor phenomena and characterization Maximum likelihood estimation Local government Gaussian noise Closed-form solution Statistical distributions Additive noise

来源：评论

学校读者我要写书评

暂无评论

Maximum-likelihood expectation-maximization algorithm for improved clinical SPECT scintimammography

Maximum-likelihood expectation-maximization algorithm for im...

引用

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Krol D.H. Feiglin W. Lee V.R. Kunniyur K.R. Gangal I.L. Coman E.D. Lipson D.A. Karczewski F.D. Thomas Department of Radiology State University of New York Upstate Medical University Syracuse NY USA

Conventional MLEM and OSEM algorithms used in SPECT Tc-99m sestamibi scintimammography produce hot spot artifacts (HSA). We investigated a suitable modification of MLEM and OSEM algorithms needed to reduce HSA. Patients with suspicious breast lesions were administered 10 mCi of Tc-99m sestamibi and SPECT scans with patients in prone position with uncompressed breasts were acquired. In addition, to simulate breast lesions, some patients were imaged with a number of breast skin markers each containing 1 /spl mu/Ci of Tc-99m. We modified MLEM and OSEM algorithms by removing from the backprojection step the rays that traverse the periphery of the support region on the way to a detector bin when their path length trough this region is shorter than some preset critical length. Such very short paths result in a very low projection counts contributed to the detector bin and this in turn gives rise to a overestimation of the activity in the peripheral voxels in the backprojection step, thus creating HSA. We analyzed the breast-lesion contrast and suppression of HSA in the images reconstructed using conventional and modified MLEM and OSEM algorithms vs. critical path length (CPL). For CPL/spl ges/0.01 pixel size, we observed improved breast-lesion contrast and lower noise in the images reconstructed, and a very significant reduction of HSA in the maximum intensity projection (MIP) images.

关键词： expectation-maximization algorithms Breast Lesions Detectors Image reconstruction Maximum likelihood detection Skin Image analysis Algorithm design and analysis Pixel

来源：评论

学校读者我要写书评

暂无评论

A fast expectation-maximum algorithm for fine-scale QTL mapping

引用

THEORETICAL AND APPLIED GENETICS 2012年第8期125卷 1727-1734页

作者： Fang, Ming Heilongjiang Bayi Agr Univ Life Sci Coll Daqing 163319 Peoples R China

The recent technology of the single-nucleotide-polymorphism (SNP) array makes it possible to genotype millions of SNP markers on genome, which in turn requires to develop fast and efficient method for fine-scale quantitative trait loci (QTL) mapping. The single-marker association (SMA) is the simplest method for fine-scale QTL mapping, but it usually shows many false-positive signals and has low QTL-detection power. Compared with SMA, the haplotype-based method of Meuwissen and Goddard who assume QTL effect to be random and estimate variance components (VC) with identity-by-descent (IBD) matrices that inferred from unknown historic population is more powerful for fine-scale QTL mapping;furthermore, their method also tends to show continuous QTL-detection profile to diminish many false-positive signals. However, as we know, the variance component estimation is usually very time consuming and difficult to converge. Thus, an extremely fast EMF (expectation-maximization algorithm under Fixed effect model) is proposed in this research, which assumes a biallelic QTL and uses an expectation-maximization (EM) algorithm to solve model effects. The results of simulation experiments showed that (1) EMF was computationally much faster than VC method;(2) EMF and VC performed similarly in QTL detection power and parameter estimations, and both outperformed the paired-marker analysis and SMA. However, the power of EMF would be lower than that of VC if the QTL was multiallelic.

关键词： LOCUS in plant genetics PLANT gene mapping SINGLE nucleotide polymorphisms GENETIC markers in plants COMPARATIVE studies expectation-maximization algorithms PARAMETER estimation

来源：评论

学校读者我要写书评

暂无评论

Adjacency-based regularization for partially ranked data with non-ignorable missing

引用

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年第0期145卷 106905-000页

作者： Nakamura, Kento Yano, Keisuke Komaki, Fumiyasu Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan RIKEN Ctr Brain Sci 2-1 Hirosawa Wako Saitama 3510198 Japan

In analyzing ranked data, we often encounter situations in which data are partially ranked. Regarding partially ranked data as missing data, this paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing mechanism. We propose estimators for both complete rankings and missing mechanisms together with a simple estimation procedure. The proposed procedure leverages the structured regularization based on an adjacency structure behind partially ranked data as well as the expectation-maximization algorithm. The experimental results demonstrate that the proposed estimator works well under non-ignorable missing mechanisms. Published by Elsevier B.V.

关键词： Alternating Direction Method of Multipliers expectation-maximization algorithms Kendall distances Mallows models

来源：评论

学校读者我要写书评

暂无评论

Identification of core-periphery structure in networks

引用

Physical Review E 2015年第3期91卷 032803-032803页

作者： Xiao Zhang Travis Martin M. E. J. Newman Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor Michigan 48109 USA Center for the Study of Complex Systems University of Michigan Ann Arbor Michigan 48109 USA

Many networks can be usefully decomposed into a dense core plus an outlying, loosely connected periphery. Here we propose an algorithm for performing such a decomposition on empirical network data using methods of statistical inference. Our method fits a generative model of core-periphery structure to observed data using a combination of an expectation-maximization algorithm for calculating the parameters of the model and a belief propagation algorithm for calculating the decomposition itself. We find the method to be efficient, scaling easily to networks with a million or more nodes, and we test it on a range of networks, including real-world examples as well as computer-generated benchmarks, for which it successfully identifies known core-periphery structure with low error rate. We also demonstrate that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak. There is no such transition for core-periphery structure, which is detectable, albeit with some statistical error, no matter how weak it is.

关键词： STATISTICAL physics expectation-maximization algorithms DECOMPOSITION (Mathematics) ERROR rates DISTRIBUTION (Probability theory)

来源：评论

学校读者我要写书评

暂无评论

The EM Method in a Probabilistic Wavelet-Based MRI Denoising

引用

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015年第1期2015卷 1-21页

作者： Martin-Fernandez, Marcos Villullas, Sergio Escuela Tecn Super Ingn Telecomun Lab Proc Imagen Valladolid 47011 Spain Univ Valladolid Fac Ciencias Dept Algebra Anal Matemat Geometria & Topol E-47011 Valladolid Spain

Human body heat emission and others external causes can interfere in magnetic resonance image acquisition and produce noise. In this kind of images, the noise, when no signal is present, is Rayleigh distributed and its wavelet coefficients can be approximately modeled by a Gaussian distribution. Noiseless magnetic resonance images can be modeled by a Laplacian distribution in the wavelet domain. This paper proposes a new magnetic resonance image denoising method to solve this fact. This method performs shrinkage of wavelet coefficients based on the conditioned probability of being noise or detail. The parameters involved in this filtering approach are calculated by means of the expectation maximization (EM) method, which avoids the need to use an estimator of noise variance. The efficiency of the proposed filter is studied and compared with other important filtering techniques, such as Nowak's, Donoho-Johnstone's, Awate-Whitaker's, and nonlocal means filters, in different 2D and 3D images.

关键词： expectation-maximization algorithms PROBABILITY theory WAVELETS (Mathematics) SIGNAL denoising MAGNETIC resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Adaptive Autoregressive Model for Reduction of Noise in SPECT

引用

COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015年第1期2015卷 1-9页

作者： Takalo, Reijo Hytti, Heli Ihalainen, Heimo Sohlberg, Antti Oulu Univ Hosp OYS Dept Diagnost Radiol Div Nucl Med Oulu 90029 Finland Tampere Univ Technol Dept Automat Sci & Engn FIN-33101 Tampere Finland Joint Author Paijat Hame Social & Hlth Care Dept Clin Physiol & Nucl Med Lahti 15850 Finland

This paper presents improved autoregressive modelling (AR) to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM) reconstruction images (AR-OSEM-ARmethod). The performance of this method was compared with filtered back projection (FBP) preceded by Butterworth filtering (BW-FBP method) and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BWmethod). A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR%) and the full width at half maximum (FWHM) of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot

关键词： AUTOREGRESSION (Statistics) NOISE control SINGLE-photon emission computed tomography IMAGE processing expectation-maximization algorithms IMAGE reconstruction

来源：评论

学校读者我要写书评

暂无评论

3-D Object Recognition Using 2-D Views

引用

IEEE TRANSACTIONS ON IMAGE PROCESSING 2008年第11期17卷 2236-2255页

作者： Li, Wenjing Bebis, George Bourbakis, Nikolaos G. Univ Nevada Dept Comp Sci & Engn Reno NV 89557 USA Wright State Univ Inst Informat Technol Dayton OH 45435 USA

We consider the problem of recognizing 3-D objects from 2-D images using geometric models and assuming different viewing angles and positions. Our goal is to recognize and localize instances of specific objects (i.e., model-based) in a scene. This is in contrast to category-based object recognition methods where the goal is to search for instances of objects that belong to a certain visual category (e.g., faces or cars). The key contribution of our work is improving 3-D object recognition by integrating Algebraic Functions of Views (AFoVs), a powerful framework for predicting the geometric appearance of an object due to viewpoint changes, with indexing and learning. During training, we compute the space of views that groups of object features can produce under the assumption of 3-D linear transformations, by combining a small number of reference views that contain the object features using AFoVs. Unrealistic views (e.g., due to the assumption of 3-D linear transformations) are eliminated by imposing a pair of rigidity constraints based on knowledge of the transformation between the reference views of the object. To represent the space of views that an object can produce compactly while allowing efficient hypothesis generation during recognition, we propose combining indexing with learning in two stages. In the first stage, we sample the space of views of an object sparsely and represent information about the samples using indexing. In the second stage, we build probabilistic models of shape appearance by sampling the space of views of the object densely and learning the manifold formed by the samples. Learning employs the expectation-maximization (EM) algorithm and takes place in a "universal," lower-dimensional, space computed through Random Projection (RP). During recognition, we extract groups of point features from the scene and we use indexing to retrieve the most feasible model groups that might have produced them (i.e., hypothesis generation). The likelihood

关键词： OPTICAL pattern recognition THREE-dimensional imaging IMAGING systems COMPUTER vision ALGEBRAIC functions expectation-maximization algorithms

来源：评论

学校读者我要写书评

暂无评论

Hedge Funds: The Good, the Bad, and the Lucky

引用

JOURNAL OF FINANCIAL AND QUANTITATIVE ANALYSIS 2017年第3期52卷 1081-1109页

作者： Chen, Yong Cliff, Michael Zhao, Haibei Texas A&M Univ Mays Business Sch College Stn TX 77843 USA Anal Grp Washington DC USA Lehigh Univ Coll Business & Econ Bethlehem PA USA

We develop an estimation approach based on a modified expectation-maximization (EM) algorithm and a mixture of normal distributions associated with skill groups to assess performance in hedge funds. By allowing luck to affect both skilled and unskilled funds, we estimate the number of skill groups, the fraction of funds from each group, and the mean and variability of skill within each group. For each individual fund, we propose a performance measure combining the fund's estimated alpha with the cross-sectional distribution of fund skill. In out-of-sample tests, an investment strategy using our performance measure outperforms those using estimated alpha and t-statistic.

关键词： MATHEMATICAL models Financial performance Gaussian distribution Investment policy Hedge funds expectation-maximization algorithms Ability Performance evaluation -- Methodology Cross-sectional method

来源：评论

学校读者我要写书评

暂无评论

An Adaptive EM Algorithm for the Maximum Likelihood Estimation of Non-Homogeneous Poisson Process Software Reliability Growth Models

引用

INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING 2017年第4期24卷 1750020-1750020页

作者： Nagaraju, Vidhyashree Fiondella, Lance Zeephongsekul, Panlop Wandji, Thierry Univ Massachusetts Dartmouth Dept Elect & Comp Engn N Dartmouth MA 02740 USA RMIT Univ Sch Math & Geospatial Sci Melbourne Vic 3001 Australia Naval Air Syst Command Patuxent River MD 20670 USA

Non-homogeneous Poisson process (NHPP) software reliability growth models (SRGMa) enable quantitative metrics to guide decisions during the software engineering life cycle, including test resource allocation and release planning. However, many SRGM possess complex mathematical forms that make them difficult to apply. Specifically, traditional procedures solve a system of nonlinear equations to identify the numerical parameters that best characterize failure data. Recently, researchers have developed expectation-maximization (EM) algorithms for NHPP SRGM that exhibit better convergence properties and can therefore find maximum likelihood estimates with greater ease. This paper presents an adaptive EM (AEM) algorithm, which combines an earlier EM algorithm for NHPP SRGM with unconstrained search of the model parameter space. Our performance analysis shows that the AEM outperforms state-of-the-art EM algorithms for NHPP SRGM with very strong statistical significance, which is as much as hundreds of times faster on some data sets. Thus, the approach can fit SRGM very quickly. We also incorporate this high performance adaptive EM algorithm into a heuristic nested model selection procedure to objectively select a model of least complexity that best characterizes the failure data. Results indicate this heuristic approach often identifies the model possessing the best model selection criteria.

关键词： Software reliability expectation-maximization algorithms stochastic processes non-homogeneous Poisson process statistical inference

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：