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Robust global identification of linear parameter varying systems with generalised expectation-maximisation algorithm

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IET CONTROL THEORY AND APPLICATIONS 2015年第7期9卷 1103-1110页

作者： Yang, Xianqiang Lu, Yaojie Yan, Zhibin Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2G6 Canada Harbin Inst Technol Nat Sci Res Ctr Harbin 150080 Heilongjiang Peoples R China

In this study, a robust approach to global identification of linear parameter varying (LPV) systems in an input-output setting is proposed. In practice, the industrial process data are often contaminated with outliers. In order to handle outliers in process modelling, the robust LPV modelling problem is formulated and solved in the scheme of generalised expectation-maximisation (GEM) algorithm. The measurement noise is taken to follow the Student's t-distribution instead of using the conventional Gaussian distribution, in this algorithm. The extent of robustness of the proposed approach is adaptively adjusted by optimising the degrees of freedom parameter of the Student's t-distribution iteratively through the maximisation step of the GEM algorithm. The numerical example is provided to demonstrate the effectiveness of the proposed approach.

关键词： expectation-maximisation algorithm Degree of freedom outliers linear parameter varying system Generalized EM algorithm Student\'s t-distribution Gaussian distribution Robustness Noise measurement

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On the application of the expectation-maximisation algorithm to the relative sensor registration problem

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IET RADAR SONAR AND NAVIGATION 2013年第2期7卷 191-203页

作者： Fortunati, Stefano Gini, Fulvio Farina, Alfonso Graziano, Antonio Greco, Maria S. Giompapa, Sofia Univ Pisa Dept Ingn Informaz I-56122 Pisa Italy SELEX ES I-00123 Rome Italy

An important prerequisite for successful multisensor integration is that the data from the reporting sensors are transformed to a common reference frame free of systematic or registration bias errors. The relative sensor registration (or grid-locking) process aligns remote data to local data under the assumption that the local data are bias free and that all biases reside with the remote sensor. In this study, an algorithm based on the expectation-maximisation approach is proposed to estimate all the registration errors involved in the grid-locking problem, that is, attitude, measurement and position biases. Its statistical performance is investigated by Monte Carlo simulation and compared with that of a previously derived linear least squares estimator and to the hybrid Cramer-Rao lower bound.

关键词： Monte Carlo methods optimisation sensor fusion expectation-maximisation algorithm relative sensor registration problem multisensor integration registration bias errors grid-locking process Monte Carlo simulation Cramér–Rao lower bound

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Estimating fluorescence lifetimes using the expectation-maximisation algorithm

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ELECTRONICS LETTERS 2018年第1期54卷 14-15页

作者： Gao, Kai Li, David Day-Uei Univ Strathclyde Ctr Biophoton Glasgow Lanark Scotland Natl Univ Def Technol Sch Elect Sci Changsha Hunan Peoples R China

The expectation-maximisation (EM) algorithm uses incomplete data to get the estimation of the probabilistic model parameter, and it has been widely used in machine learning. EM techniques are applied to estimate fluorescence lifetimes in time-correlated single-photon counting based fluorescence lifetime imaging experiments without measuring the instrument response functions. The results of Monte Carlo simulations indicate that the proposed approach can obtain better or comparable accuracy and precision performances than the previously reported method.

关键词： expectation-maximisation algorithm fluorescence medical image processing probability Monte Carlo methods

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Global implicit function theorems and the online expectation-maximisation algorithm

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AUSTRALIAN & NEW ZEALAND JOURNAL OF STATISTICS 2022年第2期64卷 255-281页

作者： Hien Duy Nguyen Forbes, Florence Univ Queensland Sch Math & Phys St Lucia Qld 4067 Australia La Trobe Univ Dept Math & Stat Bundoora Vic 3086 Australia Univ Grenoble Alpes LJK Grenoble INP CNRSInria F-38000 Grenoble France

The expectation-maximisation (EM) algorithm framework is an important tool for statistical computation. Due to the changing nature of data, online and mini-batch variants of EM and EM-like algorithms have become increasingly popular. The consistency of the estimator sequences that are produced by these EM variants often rely on an assumption regarding the continuous differentiability of a parameter update function. In many cases, the parameter update function is not in closed form and may only be defined implicitly, which makes the verification of the continuous differentiability property difficult. We demonstrate how a global implicit function theorem can be used to verify such properties in the cases of finite mixtures of distributions in the exponential family, and more generally, when the component-specific distributions admit data augmentation schemes, within the exponential family. We then illustrate the use of such a theorem in the cases of mixtures of beta distributions, gamma distributions, fully visible Boltzmann machines and Student distributions. Via numerical simulations, we provide empirical evidence towards the consistency of the online EM algorithm parameter estimates in such cases.

关键词： expectation-maximisation algorithm global implicit function theorem mixture models online algorithm student distribution

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Non-parametric mixture model with TV spatial regularisation and its dual expectation maximisation algorithm

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IET IMAGE PROCESSING 2018年第9期12卷 1655-1662页

作者： Yan, Shi Yu, Zihao Liu, Jun Beijing Normal Univ Sch Math Sci Lab Math & Complex Syst Minist Educ China Beijing 100875 Peoples R China

An image segmentation method based on a non-parametric mixture model together with total variation (TV) regularisation is proposed. The authors use a kernel density estimator as a basic mixture model, which can better separate the non-central distributed data. To enforce its robustness, they integrate the well-known TV regularisation into the statistical method. They use the dual method to efficiently solve the TV-related energy and get a new dual expectation maximisation algorithm. Experiments on both synthetic images and real images show that the proposed algorithm can achieve good segmentation results. Compared with the parametric models and hidden Markov random field-based method, the proposed method can produce better result in some cases.

关键词： medical image processing Markov processes expectation-maximisation algorithm image segmentation statistical analysis nonparametric mixture model TV spatial regularisation dual expectation maximisation algorithm image segmentation method total variation regularisation kernel density estimator basic mixture model noncentral distributed data well-known TV regularisation statistical method dual method TV-related energy parametric models

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Direction of arrival estimation for the uniform or non-uniform noise with adaptive expectation maximisation algorithm

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IET RADAR SONAR AND NAVIGATION 2020年第7期14卷 1029-1038页

作者： Zhang, Fuqiang Zhang, Zenghui Yu, Wenxian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Intelligent Sensing & Recognit Shanghai Peoples R China

The authors consider the problem of direction-of-arrival (DOA) estimation for the uniform noise (UN) or non-UN (NUN) in a passive radar. The radar waveform to be estimated is modelled as a deterministic and unknown process. Under this condition, the well-known expectation maximisation (EM) algorithm can be utilised for the estimation. In the conventional EM algorithm, the DOA is updated in every iteration, whereas the noise power ratio for each signal component is kept constant. Hence, they have to specify the noise power ratio before implementing the EM algorithm. Clearly, the DOA estimation performance of the conventional EM algorithm is sensitive to this initial noise power ratio. To deal with this problem, they propose the adaptive EM (AEM) algorithms for the UN and NUN, respectively. In the authors' proposed AEM algorithm, the noise power and DOA are jointly estimated and updated in each iteration, which indicates that the noise power ratio for each signal component would be adaptively changed. Therefore, the DOA performance of their proposed AEM algorithm is less sensitive to the initial noise power ratio, thereby achieving better estimation results. Finally, extensive experiments are carried out to validate the effectiveness of their proposed AEM algorithm.

关键词： direction-of-arrival estimation passive radar iterative methods expectation-maximisation algorithm adaptive expectation maximisation algorithm direction-of-arrival estimation signal component DOA estimation performance initial noise power ratio AEM algorithm uniform noise estimation nonuniform noise estimation NUN estimation UN estimation passive radar waveform estimation

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Precoding in downlink multi-carrier code division multiple access systems using expectation maximisation algorithm

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IET COMMUNICATIONS 2008年第10期2卷 1279-1288页

作者： Haghbin, A. Azmi, P. Tarbiat Modares Univ Dept Elect Engn Tehran Iran

The presence of multiple access interference and hardware complexity of the mobile terminal are two major burdens for multi-carrier code division multiple access schemes. Both burdens can be overcome in downlink by precoding the transmitted signal using the knowledge of the channel state information. In multi-user precoding techniques, the transmitter is optimised to combat channel impairments through the use of new spreading sequences that are obtained by solving an optimisation problem based on some criterion. Among these optimisation problems, the problem based on maximum likelihood is hard and complex to solve. In the proposed precoding scheme, the well-suited expectation maximisation algorithm is used to solve this problem. The proposed precoding technique is simulated and its performance is analysed and compared with some other precoding and detecting techniques. The results show that the proposed scheme considerably outperforms the previous precoding schemes.

关键词： multiuser precoding techniques maximum likelihood decoding multiple access interference mobile terminal code division multiple access hardware complexity radiofrequency interference expectation maximisation algorithm optimisation problem precoding mobile radio expectation-maximisation algorithm channel state information downlink multicarrier code division multiple access systems multiuser channels

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Metabolic flux estimation from incomplete labelling measurements using the expectation/conditional-maximisation algorithm

Metabolic flux estimation from incomplete labelling measurem...

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IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

作者： Wongsa, S. Kadirkamanathan, V. Billings, S. A. Wright, P. C. Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Univ Sheffield Dept Chem & Proc Engn Sheffield S1 3JD S Yorkshire England

ISBN: (纸本)9781424407101

In this work, the problem of metabolic flux estimation is formulated as a problem of parameter estimation from incomplete labelling data. The expectation/conditional maximisation (ECM) algorithm is used to determined a maximum-likelihood (ML) estimate because of its simplicity and stable convergence. We propose to simplify a nonlinear inverse problem, generally numerically solved by an iterative optimisation algorithm, to a linear regression problem which is arrived at from a hnear-in-the- parameter formulation during a partial optimisation process of the ECM algorithm. Three linear least square algorithms, the ordinary least squares (LS), the total least squares (TLS) and the constrained least squares (CLS), have been tested to solve the linear regression in this step. Using simulations, resulting parameter estimates and errors in flux estimation are compared and evaluated. The performance of the algorithms are investigated under two scenarios;when the labelling data are corrupted by a wide range of noise and when the labelling data are incompletely observed. Results suggest that the estimates from the ECM algorithm using CLS produce results superior to other combinations and have potential to be refined to improve its performance in metabolic flux estimation.

关键词： biology expectation-maximisation algorithm inverse problems least squares approximations optimisation parameter estimation regression analysis expectation/conditional-maximisation algorithm incomplete labelling data incomplete labelling measurements iter expectation-maximisation algorithm ITER TOKAMAK Parameter estimation Inverse method Biological Science Disciplines biology regression analysis algorithms label data

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An expectation maximisation algorithm for Behaviour Analysis in Video 18

An Expectation Maximisation Algorithm for Behaviour Analysis...

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18th International Conference on Information Fusion (Fusion)

作者： Isupova, Olga Mihaylova, Lyudmila Kuzin, Danil Markarian, Garik Septier, Francois Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England Univ Lancaster Sch Comp & Commun Lancaster England Telecom Lille Inst Mines Telecom Villeneuve Dascq France

ISBN: (纸本)9780982443866

Surveillance systems require advanced algorithms able to make decisions without a human operator or with minimal assistance from human operators. In this paper we propose a novel approach for dynamic topic modeling to detect abnormal behaviour in video sequences. The topic model describes activities and behaviours in the scene assuming behaviour temporal dynamics. The new inference scheme based on an expectation-maximisation algorithm is implemented without an approximation at intermediate stages. The proposed approach for behaviour analysis is compared with a Gibbs sampling inference scheme. The experiments both on synthetic and real data show that the model, based on expectation-maximisation approach, outperforms the one, based on Gibbs sampling scheme.

关键词： expectation-maximisation algorithm Videotapes behavioral research Gibbs Sampling abnormal behavior SURVEILLANCE SYSTEMS Scheme observational data

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Bilayered Anatomically-constrained Split-and-Merge expectation maximisation algorithm (BiASM) for Brain Segmentation

Bilayered Anatomically-constrained Split-and-Merge Expectati...

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Conference on Medical Imaging - Image Processing

作者： Sudre, Carole H. Cardoso, M. Jorge Ourselin, Sebastien UCL CMIC London England UCL DRC London England

ISBN: (纸本)9780819498274

Dealing with pathological tissues is a very challenging task in medical brain segmentation. The presence of pathology can indeed bias the ultimate results when the model chosen is not appropriate and lead to mis-segmentations and errors in the model parameters. Model fit and segmentation accuracy are impaired by the lack of flexibility of the model used to represent the data. In this work, based on a finite Gaussian mixture model, we dynamically introduce extra degrees of freedom so that each anatomical tissue considered is modelled as a mixture of Gaussian components. The choice of the appropriate number of components per tissue class relies on a model selection criterion. Its purpose is to balance the complexity of the model with the quality of the model fit in order to avoid overfitting while allowing flexibility. The parameters optimisation, constrained with the additional knowledge brought by probabilistic anatomical atlases, follows the expectation maximisation (EM) framework. Split-and-merge operations bring the new flexibility to the model along with a data-driven adaptation. The proposed methodology appears to improve the segmentation when pathological tissue are present as well as the model fit when compared to an atlas-based expectation maximisation algorithm with a unique component per tissue class. These improvements in the modelling might bring new insight in the characterisation of pathological tissues as well as in the modelling of partial volume effect.

关键词： expectation-maximisation algorithm segmentation Gaussian mixture model model selection split-and-merge operations pathologies

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