检索结果-内蒙古大学图书馆

Asymptotic Convergence Properties of the EM algorithm for Mixture of Experts

NEURAL COMPUTATION 2011年第8期23卷 2140-2168页

作者： Yang, Yan Ma, Jinwen Peking Univ Sch Math Sci Dept Informat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China

Mixture of experts (ME) is a modular neural network architecture for supervised classification. The double-loop expectation-maximization (EM) algorithm has been developed for learning the parameters of the ME architecture, and the iteratively reweighted least squares (IRLS) algorithm and the Newton-Raphson algorithm are two popular schemes for learning the parameters in the inner loop or gating network. In this letter, we investigate asymptotic convergence properties of the EM algorithm for ME using either the IRLS or Newton-Raphson approach. With the help of an overlap measure for the ME model, we obtain an upper bound of the asymptotic convergence rate of the EM algorithm in each case. Moreover, we find that for the Newton approach as a specific Newton-Raphson approach to learning the parameters in the inner loop, the upper bound of asymptotic convergence rate of the EM algorithm locally around the true solution Theta* is o(e(0.5-epsilon)(Theta*)), where epsilon > 0 is an arbitrarily small number, o(x) means that it is a higher-order infinitesimal as x -> 0, and e(Theta*) is a measure of the average overlap of the ME model. That is, as the average overlap of the true ME model with large sample tends to zero, the EM algorithm with the Newton approach to learning the parameters in the inner loop tends to be asymptotically superlinear. Finally, we substantiate our theoretical results by simulation experiments.

关键词： Converge internal recycling expectation-maximisation algorithm asymptotic

来源：评论

学校读者我要写书评

暂无评论

Estimating odds ratios adjusting for misclassification in Alzheimer's disease risk factor assessment

引用

STATISTICS IN MEDICINE 2000年第11-12期19卷 1523-1530页

作者： Emsley, CL Gao, S Hall, KS Hendrie, HC Indiana Univ Sch Med Div Biostat Indianapolis IN 46202 USA Indiana Univ Sch Med Dept Psychiat Indianapolis IN 46202 USA

Epidemiological studies of Alzheimer's disease and dementia are often two-phase studies including a screening phase and a clinical assessment phase. It is common to interview a relative of the subject at each of these phases to obtain information about the subject's exposure to risk factors. This can result in a misclassification error when assessing risk factors, as the two responses of the relative often differ. This is especially a problem for risk factors involving life-style and family history which cannot be confirmed using the subject's medical records. A naive analysis using data from each phase separately would give two different estimates of the odds ratio;both estimates could be biased. In this paper, we extend the estimation methods adjusting for misclassification developed by Liu and Liang to data collected through two-phase sampling. We first use a latent class analysis and the EM algorithm to estimate the misclassification parameters. We then derive the maximum pseudo-likelihood estimators, conditional on the misclassification parameters, to estimate the odds ratios accounting for the complex sampling study design. We propose to use the jack-knife estimator for estimation of the variances. We apply the above method to data collected in the Indianapolis-Ibadan Dementia Study to estimate the odds ratio for smoking adjusting for misclassification error. Copyright (C) 2000 John Wiley & Sons, Ltd.

关键词： Alzheimer Disease epidemiology Neuropsychological Tests 神经心理学测验 expectation-maximisation algorithm Statistical analysis PHASE STUDIES Misclassification Neuropsychological Tests Risk Factors Odds Ratio

来源：评论

学校读者我要写书评

暂无评论

Blind detection of severely blurred 1D Barcode

Blind detection of severely blurred 1D Barcode

引用

IEEE Global Telecommunications Conference (GLOBECOM 2010)

作者： Dridi, Noura Delignon, Yves Sawaya, Wadih Septier, Francois Univ Lille Nord France Inst TELECOM TELECOM Lille1 LAGISFRE 3303CNRS F-59653 Villeneuve Dascq France

ISBN: (纸本)9781424456383

In this paper, we present a joint blind channel estimation and symbol detection for decoding a blurred and noisy 1D barcode captured image. From an information transmission point of view, we show that the channel impulse response, the noise power and the symbols can be efficiently estimated by taking into account the signal structure such as the cyclostationary property of the hidden Markov process to estimate. Based on the expectation-maximisation method, we show that the new algorithm offers significative performance gain compared to classical ones pushing back the frontiers of the barcode technology.

关键词： blind detection channel estimation channel impulse response expectation-maximisation algorithm expectation-maximisation method hidden Markov models hidden Markov process image denoising image restoration joint blind channel estimation noisy 1D barcode cap

来源：评论

学校读者我要写书评

暂无评论

Dimensionality Reduction for Probabilistic Movement Primitives 14

Dimensionality Reduction for Probabilistic Movement Primitiv...

引用

14th IEEE-RAS International Conference on Humanoid Robots (Humanoids)

作者： Colome, Adria Neumann, Gerhard Peters, Jan Torras, Carme UPC CSIC Inst Robot Inf Ind Barcelona Spain TUD Intelligent Autonomous Syst Lab Darmstadt Germany Max Planck Inst Intelligent Syst Tubingen Germany

ISBN: (纸本)9781479971749

Humans as well as humanoid robots can use a large number of degrees of freedom to solve very complex motor tasks. The high-dimensionality of these motor tasks adds difficulties to the control problem and machine learning algorithms. However, it is well known that the intrinsic dimensionality of many human movements is small in comparison to the number of employed DoFs, and hence, the movements can be represented by a small number of synergies encoding the couplings between DoFs. In this paper, we want to apply Dimensionality Reduction (DR) to a recent movement representation used in robotics, called Probabilistic Movement Primitives (ProMP). While ProMP have been shown to have many benefits, they suffer with the high-dimensionality of a robotic system as the number of parameters of a ProMP scales quadratically with the dimensionality. We use probablistic dimensionality reduction techniques based on expectation maximization to extract the unknown synergies from a given set of demonstrations. The ProMP representation is now estimated in the low-dimensional space of the synergies. We show that our dimensionality reduction is more efficient both for encoding a trajectory from data and for applying Reinforcement Learning with Relative Entropy Policy Search (REPS).

关键词： entropy expectation-maximisation algorithm humanoid robots learning (artificial intelligence) mobile robots DR ProMP REPS dimensionality reduction expectation maximization humanoid robot probabilistic movement primitives reinforcement learning relative entropy policy search Context Principal component analysis Probabilistic logic Robot kinematics Trajectory Vectors Humanoid robots dimensionality reduction expectation-maximisation algorithm renewable energy power generation Robot kinematics Probabilistic logic learning (artificial intelligence) trajectory Mobile robots DR principal components analysis entropy Movements

来源：评论

学校读者我要写书评

暂无评论

RETRACTED: Neural network based identification of meat spoilage using Fourier-transform infrared spectra (Retracted article. See vol. 272, 2020)

引用

JOURNAL OF FOOD ENGINEERING 2014年 142卷 118-131页

作者： Kodogiannis, Vassilis S. Kontogianni, Eva Lygouras, John N. Univ Westminster Fac Sci & Technol Computat Intelligence Grp London W1W 6UW England Reg Unit Herakl Gen Directorate Dev GR-71201 Iraklion Greece Democritus Univ Thrace Dept Elect & Comp Engn GR-67100 Xanthi Greece

To address the rapid and non-destructive detection of meat spoilage microorganisms during aerobic storage at chill and abuse temperatures, Fourier transform infrared spectroscopy (FTIR) with the help of an intelligent-based identification system was attempted in this work. The objective of this study is to associate simultaneously spectral data with microbiological data (log counts), for Total Viable Counts, Pseudomonas spp., Brochothrix thermosphacta, Lactic Acid Bacteria and Enterobacteriaceae. The dual purpose of the proposed modelling scheme is not only to classify meat samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from FTIR spectra. An Extended Normalised Radial Basis Function neural network has been implemented, and the Bayesian Ying-Yang expectation maximisation algorithm has been utilised together with novel splitting operations to determine network's size and parameter set. The dimensionality reduction of spectral data has been addressed by the implementation of a fuzzy principal component algorithm. Results confirmed the superiority of the adopted methodology compared to other schemes such as multilayer perceptron and the partial least squares techniques and indicated that spectral information obtained by FTIR spectroscopy during beef spoilage, in combination with an efficient choice of a learning-based modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Neural networks expectation-maximisation algorithm FTIR Fuzzy principal components analysis Meat spoilage Pattern recognition Modelling

来源：评论

学校读者我要写书评

暂无评论

Image segmentation by a new weighted Student's t-mixture model

引用

IET IMAGE PROCESSING 2013年第3期7卷 240-251页

作者： Zhang, Hui Wu, Qing Ming Jonathan Thanh Minh Nguyen Nanjing Univ Informat Sci & Technol Sch Comp & Software Nanjing Jiangsu Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Nanjing Univ Informat Sci & Technol Jiangsu Engn Ctr Network Monitoring Nanjing Jiangsu Peoples R China

In this study, the authors introduce a new weighted Student's t-mixture model (WSMM) for image segmentation. Gaussian distribution and Student's t-distribution are the two commonly used probabilities in the finite mixture model (FMM). The Student's t-mixture model has come to be regarded as an alternative to Gaussian mixture models, as it is heavily tailed and more robust for outliers. Moreover, the pixels are considered independent of each other in the FMM. Although some existing methods incorporate the spatial relationship between neighbouring pixels, they do not consider the relationship between spatial information and clustering information, thus those reported methods remain sensitive to noise. The advantages of the authors method are as follows: first, the authors introduce WSMM to incorporate the local spatial information, pixel intensity value and clustering information in an image. Second, the authors model is simple, easy to implement and has a good balance between noise insensitiveness and image detail preservation. Third, they adopt the gradient method and expectation maximisation algorithm, which allow for simultaneous estimation of optimal parameters. Finally, the most useful statistical tool for image segmentation, the well-known hidden Markov random field model, is a special case of their model. Thus, their method is general enough for model-based techniques construction. Experimental results on synthetic and real images demonstrate the improved robustness and effectiveness of their approach.

关键词： expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images expectation-maximisation algorithm Gaussian distribution gradient methods hidden Markov models image segmentation pattern clustering image segmentation weighted student t-mixture model WSMM Gaussian distribution student t-distribution finite mixture model FMM Gaussian mixture models neighbouring pixels clustering information local spatial information pixel intensity value noise insensitiveness image detail preservation gradient method expectation maximisation algorithm statistical tool hidden Markov random field model model-based techniques construction synthetic images real images

来源：评论

学校读者我要写书评

暂无评论

Parameter estimation of G0 distribution based on improved recursive expectation-maximisation method for clutter modelling

引用

JOURNAL OF ENGINEERING-JOE 2019年第20期2019卷 6759-6762页

作者： Lu, Jiaxin Sun, Yuze Zhuo, Bangsheng Yang, Xiaopeng Beijing Inst Technol Sch Informat & Elect Beijing Key Lab Embedded Real Time Informat Proc Beijing 100081 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Modelling and simulation of clutter are important in radar signal processing, the G0 distribution is generally adopted to simulate the ground clutter in radar echoes. In order to improve the modelling accuracy of clutter modelling, the actual data should be used for model parameter estimation. However, in some special situations, the actual data sample is very small. Existing methods cannot estimate the parameters of G0 distribution efficiently. To solve this problem, an improved recursive expectation-maximisation (EM) method is proposed to estimate the parameters of clutter in this article. This method combines the expectation step and maximisation step in one equation. Through recursive method and simplification of the positive definite matrix, this proposed method can obtain maximum likelihood estimation more efficiently than the conventional EM method and recursive EM method. Simulation results show that the performance of the proposed method is better than that of the conventional methods for a small data sample.

关键词： recursive estimation radar signal processing expectation-maximisation algorithm radar clutter maximum likelihood estimation parameter estimation improved recursive expectation–maximisation method clutter modelling radar signal processing ground clutter radar echoes modelling accuracy model parameter estimation actual data sample expectation step maximisation step recursive method maximum likelihood estimation

来源：评论

学校读者我要写书评

暂无评论

Retinal vessel classification: sorting arteries and veins

Retinal vessel classification: sorting arteries and veins

引用

35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Relan, D. MacGillivray, T. Ballerini, L. Trucco, E. Univ Edinburgh Clin Res Imaging Ctr Edinburgh EH8 9YL Midlothian Scotland

ISBN: (纸本)9781457702167

For the discovery of biomarkers in the retinal vasculature it is essential to classify vessels into arteries and veins. We automatically classify retinal vessels as arteries or veins based on colour features using a Gaussian Mixture Model, an expectation-Maximization (GMM-EM) unsupervised classifier, and a quadrant-pairwise approach. Classification is performed on illumination-corrected images. 406 vessels from 35 images were processed resulting in 92% correct classification (when unlabelled vessels are not taken into account) as compared to 87.6%, 90.08%, and 88.28% reported in [12] [14] and [15]. The classifier results were compared against two trained human graders to establish performance parameters to validate the success of classification method. The proposed system results in specificity of (0.8978, 0.9591) and precision (positive predicted value) of (0.9045, 0.9408) as compared to specificity of (0.8920, 0.7918) and precision of (0.8802, 0.8118) for (arteries, veins) respectively as reported in [13]. The classification accuracy was found to be 0.8719 and 0.8547 for veins and arteries, respectively.

关键词： Gaussian distribution biomedical optical imaging blood vessels expectation-maximisation algorithm eye feature extraction image classification medical image processing retinal recognition

来源：评论

学校读者我要写书评

暂无评论

EM based extended kalman filter for estimation of rotor time-constant of induction motor

EM based extended kalman filter for estimation of rotor time...

引用

IEEE International Symposium on Industrial Electronics

作者： Radhakrishnan, K. Unnikrishnan, A. Balakrishnan, K. G. CUSAT Kochi 22 India NPOL Kochi 21 Kerala India

ISBN: (纸本)9781424404964

This paper deals with the recursive optimum estimation of rotor resistance and inductance of induction motor. The estimation of parameter and states in the presence of system noise is achieved using EKF, which takes in to account measurement and modelling inaccuracies. A major limitation in the parameter estimation using EKF is that its optimality is dependent on the choice of the right covariance matrices. In this paper the EM(expectation Maximization) algorithm along with the Kalman smoothing is used to obtain the initial values of process and measurement covariance matrices. The Vas model of the induction motor is used for simulation and the rotor inductance and rotor resistance are considered as parameters as well as states. For the estimation of these parameters, the state vector is augmented with these parameters to be estimated. The parameters used for simulation is that of a three phase, 400V, 50 Hz cage induction motor. Matlab is used to simulate the system and from the results it is observed that the parameter estimates converges to true values with a reasonable degree of accuracy.

关键词： Kalman filters expectation-maximisation algorithm induction motors nonlinear filters power filters rotors 400 V 50 Hz Kalman smoothing cage induction motors covariance matrices expectation maximization extended Kalman filter parameter estimation recursiv

来源：评论

学校读者我要写书评

暂无评论

Broadband Field Directionality Mapping with Spatially-Aliased Arrays

Broadband Field Directionality Mapping with Spatially-Aliase...

引用

IEEE 7th Sensor Array and Multichannel Signal Processing Workshop (SAM)

作者： Odom, Jonathan L. Krolik, Jeffrey L. Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA

ISBN: (纸本)9781467310710

This paper addresses the problem of broadband spatial spectrum estimation using multiple spatially-aliased arrays. Unlike previous approaches using sparse arrays, the signals here are assumed to be uncorrelated between multiple arrays which, in fact, may be receiving the same source during different time intervals. This paper presents an approach which jointly exploits spatial-orientation and broadband temporal diversity in order to estimate the spatial spectrum even when the inter-element spacing within each array is greater than a half-wavelength. A dynamical model for the spatial spectrum is employed to formulate a maximum likelihood estimate, which is computed via a recursive version of the expectation-maximization algorithm using data from different arrays. Simulation results are presented to demonstrate the ability of the method to suppress spatial grating lobes and increase low SNR target detection in an interference dominated environment.

关键词： maximum likelihood estimation arrays Spectrum Analysis broadband expectation-maximisation algorithm time diversity Time intervals

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：