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A genetic expectation-maximization method for unsupervised change detection in multitemporal SAR imagery

INTERNATIONAL JOURNAL OF REMOTE SENSING 2009年第24期30卷 6591-6610页

作者： Bazi, Yakoub Melgani, Farid Bruzzone, Lorenzo Vernazza, Gianni Univ Trent Dept Comp Sci & Informat Engn I-38050 Trento Italy Al Jouf Univ Coll Engn Al Jouf 2014 Saudi Arabia Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

In this paper, we present a novel, automatic and unsupervised change-detection approach to the analysis of single-channel single-polarization multitemporal SAR images. The statistical parameters of the changed and unchanged classes, which are assumed to follow a generalized Gaussian (GG) distribution in the analysed log-ratio image, are explicitly estimated by the expectation-maximization (EM) algorithm initialized with a robust strategy based on genetic algorithms (GAs). In addition, the proposed approach integrates two further processing capabilities. The first one intends to cope with the problem of the automatic detection of multiple changes in the scene. This is carried out by modelling the log-ratio image histogram with a multimodal GG mixture whose number of components is estimated basing on the Bayesian information criterion (BIC). The second processing capability allows exploitation of spatial contextual information in the change detection process through a Markovian formulation. Results obtained on both simulated and real data are reported and discussed.

关键词： expectation-maximization algorithms REMOTE sensing GENETIC algorithms GAUSSIAN distribution COMBINATORIAL optimization OPTICAL polarization DISTRIBUTION (Probability theory) EARTH (Planet) SURFACE

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Diagnostics for skew-normal nonlinear regression models with AR(1) errors

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 4403-4416页

作者： Xie, Feng-Chang Lin, Jin-Guan Wei, Bo-Cheng Southeast Univ Dept Math Nanjing 210096 Peoples R China Nanjing Agr Univ Dept Math Nanjing 210095 Peoples R China

In this article, we consider some diagnostics for skew-normal nonlinear regression models with AR(1) errors which provide a useful extension of the normal regression models. The estimation of the parameters in the models is studied based on the EM algorithm. Meanwhile, several score tests are presented for testing the homogeneity of the scale parameter and/or significance of autocorrelation in skew-normal nonlinear regression models. The properties of score tests are investigated through Monte Carlo simulations. The test methods are illustrated with two numerical examples. (C) 2009 Elsevier B.V. All rights reserved.

关键词： DIAGNOSIS NONLINEAR models (Statistics) REGRESSION analysis MEASUREMENT errors expectation-maximization algorithms AUTOCORRELATION (Statistics) MONTE Carlo method SIMULATION methods & models NUMERICAL analysis

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Estimating the parameters of the Marshall-Olkin bivariate Weibull distribution by EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第4期53卷 956-965页

作者： Kundu, Debasis Dey, Arabin Kumar Indian Inst Technol Dept Math & Stat Kanpur 208016 Uttar Pradesh India

in this paper we consider the Marshall-Olkin bivariate Weibull distribution. The Marshall-Olkin bivariate Weibull distribution is a singular distribution, whose both the marginals are univariate Weibull distributions. This is a generalization of the Marshall-Olkin bivariate exponential distribution. The cumulative joint distribution of the Marshall-Olkin bivariate Weibull distribution is a mixture of an absolute continuous distribution function and a singular distribution function. This distribution has four unknown parameters and it is observed that the maximum likelihood estimators of the unknown parameters cannot be obtained in explicit forms. In this paper we discuss about the computation of the maximum likelihood estimators of the unknown parameters using P EM algorithm. We perform some simulations to see the performances of the EM algorithm and re-analyze one data set for illustrative purpose. (C) 2008 Elsevier B.V. All rights reserved.

关键词： WEIBULL distribution PARAMETER estimation VARIABLES (Mathematics) GENERALIZABILITY theory (Statistics) expectation-maximization algorithms SIMULATION methods & models

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Robust probabilistic PCA with missing data and contribution analysis for outlier detection

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第10期53卷 3706-3716页

作者： Chen, Tao Martin, Elaine Montague, Gary Nanyang Technol Univ Sch Chem & Biomed Engn Singapore 637459 Singapore Univ Newcastle Sch Chem Engn & Adv Mat Newcastle Upon Tyne NE1 7RU Tyne & Wear England

Principal component analysis (PCA) is a widely adopted multivariate data analysis technique, with interpretation being established on the basis of both classical linear projection and a probability model (i.e. probabilistic PCA (PPCA)). Recently robust PPCA models, by using the multivariate t-distribution, have been proposed to consider the situation where there may be outliers within the data set. This paper presents an overview of the robust PPCA technique, and further discusses the issue of missing data. An expectation-maximization (EM) algorithm is presented for the maximum likelihood estimation of the model parameters in the presence of missing data. When applying robust PPCA for outlier detection, a contribution analysis method is proposed to identify which variables contribute the most to the occurrence of outliers, providing valuable information regarding the source of outlying data. The proposed technique is demonstrated on numerical examples, and the application to outlier detection and diagnosis in an industrial fermentation process. (C) 2009 Elsevier B.V. All rights reserved.

关键词： PRINCIPAL components analysis MISSING data (Statistics) MULTIVARIATE analysis PROBABILITY measures expectation-maximization algorithms ROBUST control NUMERICAL analysis FERMENTATION

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Mixture modeling with applications in schizophrenia research

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第7期53卷 2563-2572页

作者： Wu, Qiang Sampson, Allan R. E Carolina Univ Dept Biostat Greenville NC 27858 USA Univ Pittsburgh Dept Stat Pittsburgh PA 15260 USA

Finite mixture modeling, together with the EM algorithm, have been widely used in clustering analysis. Under such methods, the unknown group membership is usually treated as missing data. When the "complete data" (log-)likelihood function does not have an explicit solution, the simplicity of the EM algorithm breaks down. Authors, including Rai and Matthews [Rai, S.N., Matthews, D.E., 1993. Improving the em algorithm. Biometrics 49, 587-591], Lange [Lange, K., 1995a. A gradient algorithm locally equivalent to the em algorithm. Journal of the Royal Statistical Society B 57(2) 425-437], and Titterington [Titterington, D.M., 1984. Recursive parameter estimation using incomplete data. journal of the Royal Statistical Society B. 46, 257-267] developed modified algorithms therefore. As motivated by research in a large neurobiological project, we propose in this paper a new variant of such modifications and show that it is self-consistent. Moreover, simulations are conducted to demonstrate that the new variant converges faster than its predecessors. (C) 2008 Elsevier B.V. All rights reserved.

关键词： SCHIZOPHRENIA RESEARCH MATHEMATICAL models expectation-maximization algorithms CLUSTER analysis (Statistics) ESTIMATION theory MATHEMATICAL statistics

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Online updating appearance generative mixture model for meanshift tracking

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MACHINE VISION AND APPLICATIONS 2009年第3期20卷 163-173页

作者： Tu, Jilin Tao, Hai Huang, Thomas Univ Illinois Urbana & Champaign Dept Elect & Comp Engn Urbana IL 61801 USA Univ Calif Santa Cruz Dept Comp Engn Santa Cruz CA 95064 USA

This paper proposes an appearance generative mixture model based on key frames for meanshift tracking. Meanshift tracking algorithm tracks an object by maximizing the similarity between the histogram in tracking window and a static histogram acquired at the beginning of tracking. The tracking therefore could fail if the appearance of the object varies substantially. In this paper, we assume the key appearances of the object can be acquired before tracking and the manifold of the object appearance can be approximated by piece-wise linear combination of these key appearances in histogram space. The generative process is described by a Bayesian graphical model. An Online EM algorithm is proposed to estimate the model parameters from the observed histogram in the tracking window and to update the appearance histogram. We applied this approach to track human head motion and to infer the head pose simultaneously in videos. Experiments verify that our online histogram generative model constrained by key appearance histograms alleviates the drifting problem often encountered in tracking with online updating, that the enhanced meanshift algorithm is capable of tracking object of varying appearances more robustly and accurately, and that our tracking algorithm can infer additional information such as the object poses. The online version of this article (doi:10.1007/s00138-007-0115-x) contains supplementary material, which is available to authorized users.

关键词： expectation-maximization algorithms ARITHMETIC -- Foundations GRAPHICAL modeling (Statistics) MULTIVARIATE analysis -- Graphic methods ALGEBRA

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A note on the exponential Poisson distribution: A nested EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第4期53卷 894-899页

作者： Karlis, Dimitris Athens Univ Econ & Business Dept Stat Athens 10434 Greece

An EM type algorithm for estimating the parameters of the exponential Poisson distribution has recently been proposed. This algorithm uses a Newton Raphson approach at the M-step. An alternative approach, where the Newton Raphson step is replaced by another EM algorithm is described. This approach has simple closed form expressions, making use of a nested EM scheme where the M-step itself is solved with an EM algorithm. An advantage of this algorithm is that it provides estimates in the admissible range when the initial values are in the admissible range, and avoids overflow problems that may occur during Newton-Raphson iterations and, in certain cases, it can be faster. Some simulation evidence on the speed of the new approach compared to the one that uses Newton-Raphson is provided. (C) 2008 Elsevier B.V. All rights reserved.

关键词： POISSON distribution EXPONENTIAL functions expectation-maximization algorithms PARAMETER estimation NEWTON-Raphson method SIMULATION methods & models

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New global optimization algorithms for model-based clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 3999-4017页

作者： Heath, Jeffrey W. Fu, Michael C. Jank, Wolfgang Univ Maryland Robert H Smith Sch Business College Pk MD 20742 USA Ctr Coll Danville Dept Math Danville KY 40422 USA

The expectation-maximization (EM) algorithm is a very popular optimization tool for mixture problems and in particular for model-based clustering problems. However, while the algorithm is convenient to implement and numerically very stable, it only produces local solutions. Thus, it may not achieve the globally optimal solution in problems that have a large number of local optima. This paper introduces several new algorithms designed to produce global solutions in model-based clustering. The building blocks for these algorithms are methods from the operations research literature, namely the Cross-Entropy (CE) method and Model Reference Adaptive Search (MRAS). One problem with applying these methods directly is the efficient simulation of positive definite covariance matrices. We propose several new solutions to this problem. One solution is to apply the principles of expectation-maximization updating, which leads to two new algorithms, CE-EM and MRAS-EM. We also propose two additional algorithms, CE-CD and MRAS-CD, which rely on the Cholesky decomposition. We conduct numerical experiments of varying complexity to evaluate the effectiveness of the proposed algorithms in comparison to classical EM. We find that although a single run of the new algorithms is slower than a single run of EM, all have the potential for producing significantly better solutions. We also find that although repeat application of EM may achieve similar results, our algorithms provide automated, data-driven decision rules which may significantly reduce the burden of searching for the global optimum. (C) 2009 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithms NUMERICAL analysis CROSS-entropy method ANALYSIS of covariance DECOMPOSITION (Mathematics) COMPARATIVE studies

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Fast estimation of spatially dependent temporal vegetation trends using Gaussian Markov random fields

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第8期53卷 2885-2896页

作者： Bolin, David Lindstroem, Johan Eklundh, Lars Lindgren, Finn Lund Univ Ctr Math Sci SE-22100 Lund Sweden Lund Univ Dept Phys Geog & Ecosyst Anal GeoBiosphere Sci Ctr SE-22100 Lund Sweden

There is a need for efficient methods for estimating trends in spatio-temporal Earth Observation data. A suitable model for such data is a space-varying regression model, where the regression coefficients for the spatial locations are dependent. A second order intrinsic Gaussian Markov Random Field prior is used to specify the spatial covariance structure. Model parameters are estimated using the expectation Maximisation (EM) algorithm, which allows for feasible computation times for relatively large data sets. Results are illustrated with simulated data sets and real vegetation data from the Sahel area in northern Africa. The results indicate a substantial gain in accuracy compared with methods based on independent ordinary least squares regressions for the individual pixels in the data set. Use of the EM algorithm also gives a substantial performance gain over Markov Chain Monte Carlo-based estimation approaches. (c) 2008 Elsevier B.V. All rights reserved.

关键词： ESTIMATION theory VEGETATION dynamics SPATIAL analysis (Statistics) GAUSSIAN Markov random fields REGRESSION analysis ANALYSIS of covariance expectation-maximization algorithms MONTE Carlo method

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Maximum likelihood estimation in vector long memory processes via EM algorithm

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 4133-4142页

作者： Pai, Jeffrey Ravishanker, Nalini Univ Connecticut Dept Stat Storrs CT 06269 USA Univ Manitoba Warren Ctr Actuarial Studies & Res Winnipeg MB R3T 5V4 Canada

We present an approach for exact maximum likelihood estimation of parameters from univariate and multivariate autoregressive fractionally integrated moving average models with Gaussian errors using the expectation maximization (EM) algorithm. The method takes advantage of the relation between the VARFIMA(0, d, 0) process and the corresponding VARFIMA(p, d, q) process in the computation of the likelihood. (C) 2009 Elsevier B.V. All rights reserved.

关键词： MAXIMUM likelihood statistics ESTIMATION theory expectation-maximization algorithms MULTIVARIATE analysis AUTOREGRESSION (Statistics) GAUSSIAN processes

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