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Penalized factor mixture analysis for variable selection in clustered data

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 4301-4310页

作者： Galimberti, Giuliano Montanari, Angela Viroli, Cinzia Univ Bologna Dipartimento Sci Stat I-40126 Bologna Italy

A model-based clustering approach which contextually performs dimension reduction and variable selection is presented. Dimension reduction is achieved by assuming that the data have been generated by a linear factor model with latent variables modeled as Gaussian mixtures. Variable selection is performed by shrinking the factor loadings though a penalized likelihood method with an L1 penalty. A maximum likelihood estimation procedure via the EM algorithm is developed and a modified BIC criterion to select the penalization parameter is illustrated. The effectiveness of the proposed model is explored in a Monte Carlo simulation study and in a real example. (C) 2009 Elsevier B.V. All rights reserved.

关键词： MODEL ANALYZERS Analysers LU1 expectation-maximisation algorithm Monte Carlo technique maximum likelihood estimation Input variables

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Variational Bayesian learning for background subtraction based on local fusion feature

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IET COMPUTER VISION 2016年第8期10卷 884-893页

作者： Yan, Junhua Wang, Shunfei Xie, Tianxia Yang, Yong Wang, Jiayi Nanjing Univ Aeronaut & Astronaut Coll Astronaut Nanjing Jiangsu Peoples R China

To resist the adverse effect of shadow interference, illumination changes, indigent texture and scenario jitter in object detection and improve performance, a background modelling method based on local fusion feature and variational Bayesian learning is proposed. First, U-LBSP (uniform-local binary similarity patterns) texture feature, lab colour and location feature are used to construct local fusion feature. U-LBSP is modified from local binary patterns in order to reduce computational complexity and better resist the influence of shadow and illumination changes. Joint colour and location feature are introduced to deal with the problem of indigent texture and scenario jitter. Then, LFGMM (Gaussian mixture model based on local fusion feature) is updated and learned by variational Bayes. In order to adapt to dynamic changing scenarios, the variational expectation maximisation algorithm is applied for distribution parameters optimisation. In this way, the optimal number of Gaussian components as well as their parameters can be automatically estimated with less time expended. Experimental results show that the authors' method achieves outstanding detection performance especially under conditions of shadow disturbances, illumination changes, indigent texture and scenario jitter. Strong robustness and high accuracy have been achieved.

关键词： image texture object detection learning (artificial intelligence) variational techniques Bayes methods expectation-maximisation algorithm computational complexity jitter image colour analysis image fusion optimisation variational Bayesian learning background subtraction shadow interference effect illumination changes indigent texture scenario jitter object detection background modelling method U-LBSP texture feature uniform-local binary similarity pattern texture feature lab colour location feature computational complexity LFGMM Gaussian mixture model based on local fusion feature variational expectation maximisation algorithm distribution parameter optimisation Gaussian components shadow disturbances

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On some lifetime distributions with decreasing failure rate

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第12期53卷 4433-4440页

作者： Chahkandi, M. Ganjali, M. Shahid Beheshti Univ Dept Stat Tehran Iran

A new two-parameter distribution family with decreasing failure rate arising by mixing power-series distribution and exponential distribution is introduced. This family includes some well-used mixing distributions. Various properties of this family are discussed and the estimation of parameters are obtained by method of maximum likelihood. An EM algorithm is proposed for computing the estimates and expression for their asymptotic variances and covariances are derived. Simulation studies are performed and experimental results are illustrated based on real data sets. (C) 2009 Elsevier B.V. All rights reserved.

关键词： MONOTONE HAZARD RATE MAXIMUM-LIKELIHOOD ESTIMATION EXPONENTIAL-DISTRIBUTIONS EM algorithm WEIBULL DISTRIBUTION GAMMA-DISTRIBUTION CENSORED-DATA MIXTURE PARAMETERS BOUNDS censored data mixtures observational data Blending expectation-maximisation algorithm maximum likelihood Exponential distribution Parameter estimation Weibull distribution maximum likelihood estimation decreasing failure rate family

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Hidden Markov model parameters estimation with independent multiple observations and inequality constraints

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IET SIGNAL PROCESSING 2014年第9期8卷 938-949页

作者： Xia, Lisha Fang, Huajing Liu, Xiaoyong Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

In this study, the authors focus on hidden Markov model (HMM) parameters estimation with independent multiple observations and non-linear inequality constraints. The parameters estimation process is divided into four steps: initialisation, parameters pre-estimation, parameters re-estimation and termination. The pre-estimation results are used to approximate non-linear inequality constraints to linear inequality constraints. In parameters re-estimation step, the active-set optimisation is combined with the expectation maximisation (EM) algorithm in M-step and the active set-based EM algorithm is proposed to re-estimate HMM parameters when inequality constraints are not satisfied in pre-estimation. An auxiliary function is devised for reconstructing the optimisation objective function and the convergence of the proposed algorithm is also demonstrated. Simulation results indicate that the proposed algorithm provides better performance by modifying the random error of observation data appropriately and it is powerful for industry process fault diagnosis.

关键词： hidden Markov models parameter estimation nonlinear estimation approximation theory expectation-maximisation algorithm optimisation fault diagnosis hidden Markov model parameter estimation independent multiple observation HMM parameter estimation parameters pre-estimation step parameters reestimation step termination step initialisation step approximate nonlinear inequality constraint linear inequality constraint active-set optimisation expectation maximisation algorithm M-step algorithm active set-based EM algorithm optimisation objective function reconstruction random error modification industry process fault diagnosis

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Distributed maximum likelihood estimation for flow and speed density prediction in distributed traffic detectors with Gaussian mixture model assumption

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IET INTELLIGENT TRANSPORT SYSTEMS 2012年第2期6卷 215-222页

作者： Ramezani, A. Moshiri, B. Abdulhai, B. Kian, A. R. Univ Tehran Sch Elect & Comp Engn Control & Intelligent Proc Ctr Excellence Tehran Iran Univ Toronto Dept Civil Engn Toronto ITS Ctr Toronto ON Canada

In this study a distributed maximum likelihood estimator (MLE) has been presented to estimate ML function of traffic flow and mean traffic speed in a freeway. This algorithm uses traffic measurements including volume, occupancy and mean speed which gathered by some inductive loop detectors. These traffic detectors (traffic sensors) located in certain distances in the freeway network such that they establish a distributed sensor network (DSN). The presented distributed estimator has employed a distributed expectation maximisation algorithm to calculate MLE. In the E-step of this algorithm, each sensor node independently calculates local sufficient statistics by using local observations. A consensus filter is used to diffuse local sufficient statistics to neighbours and estimate global sufficient statistics in each node. In the M-step of this algorithm, each sensor node uses the estimated global sufficient statistics to update model parameters of the Gaussian mixtures, which can maximise the log-likelihood in the same way as in the standard EM algorithm. As the consensus filter only requires each node to communicate with its neighbours, the distributed algorithm is scalable and robust. A set of field traffic data from Minnesota freeway network has been used to simulate and verify the proposed distributed estimator performance.

关键词： Traffic engineering computing distributed expectation maximisation algorithm Gaussian mixture model assumption distributed traffic detector M-step Filtering methods in signal processing speed density prediction ML function estimation mean traffic speed E-step distributed maximum likelihood estimation Gaussian processes traffic measurements Digital signal processing MLE inductive loop detectors filtering theory Minnesota freeway network local sufficient statistics distributed sensors log likelihood consensus filter Sensing devices and transducers global sufficient statistics distributed sensor network road traffic traffic flow prediction Other topics in statistics automated highways traffic sensors expectation-maximisation algorithm

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ANOTHER LOOK AT A BAYESIAN DIRECT DECONVOLUTION METHOD

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SIGNAL PROCESSING 1985年第2期9卷 101-106页

作者： TITTERINGTON, DM ROSSI, C Department of Statistics University of Glasgow Glasgow G12 8QW Scotland Dipartimento di Matematica II Universita di Roma Via Orazio Raimondo 00173 Roma Italy

The paper points out that a certain iterative method of direct deconvolution is a particular manifestation of the so-called expectation-maximisation (EM) algorithm. Recognition of this leads to the establishment of general convergence results by reference to existing work. Zusammenfassung Dieser Artikel zeigt dass eine iterative direkte Unfaltungsmethode, die auf dem Bayes Theorem basiert, ein Spezialfall des E.M. algorithmus ist (expectation-Maximization). Diese Bemerkung erlaubt es, die Konvergenz mit Hilfe von Referenzen zu frueren Arbeiten zu zeigen.

关键词： Direct deconvolution expectation-maximisation algorithm mixture distributions

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Complexity control in a mixture model by the Hardy-Weinberg equilibrium

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2009年第5期53卷 1711-1719页

作者： Bingham, Ella Mannila, Heikki Univ Helsinki Helsinki Inst Informat Technol FIN-00014 Helsinki Finland Aalto Univ Helsinki Inst Informat Technol FIN-02015 Espoo Finland

A method of complexity control in multinomial mixture modeling of multiple-marker genotype data, imposing the Hardy-Weinberg equilibrium (HWE) between the genotype values, is studied. This is a very natural restriction, and known to hold at population level under modest assumptions. The hypothesis under study is that imposing this restriction will prevent overfitting and lead to a better model. This is shown to indeed be case. Experimental results an chromosomes 1 and 17 of the HapMap data demonstrate that the restricted model generalizes better to unseen data, and also finds clusters that correspond better to the ethnic groups of the HapMap, when compared with a model without the HWE restriction. (C) 2008 Elsevier B.V. All rights reserved.

关键词： MULTILOCUS GENOTYPE DATA POPULATION-STRUCTURE MAXIMUM-LIKELIHOOD BAYESIAN-ANALYSIS EM algorithm ADMIXTURE PROPORTIONS INFERENCE MARKERS GENOME Genome Complexity HapMap expectation-maximisation algorithm maximum likelihood Restriction Bayes methods Mixture models Population structure

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EM-SLAM With Inertial/Visual Applications

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2017年第1期53卷 273-285页

作者： Sjanic, Zoran Skoglund, Martin A. Gustafsson, Fredrik Linkoping Univ Dept Elect Engn Div Automat Control SE-58183 Linkoping Sweden

The general simultaneous localization and mapping (SLAM) problem aims at estimating the state of a moving platform simultaneously with map building of the local environment. Current state-of-the-art methods such as given by Williams et al. relies on nonlinear least-squares (NLS) batch formulations with structure exploitation for memory efficiency and speed. We investigate the expectation-maximization (EM) algorithm for solving a generalized version of the NLS problem. This EM-SLAM algorithm solves two simpler problems iteratively yielding a low computational complexity. The iterations switch between state estimation, which can use any state-space smoother, and map estimation, where a quasi-Newton method is suggested. The proposed method is evaluated in real experiments and also in simulations on a platform with a monocular camera attached to an inertial measurement unit. The results show that EM-SLAM has much lower computational complexity than NLS while maintaining comparable accuracy.

关键词： monocular camera maps INERTIAL MEASUREMENT UNITS Measurement units expectation-maximisation algorithm complexity classes Newton method supersonic low altitude missile simultaneous localization and mapping State estimation moving platform MAPPING(MATHEMATICAL)

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Sparse channel estimation algorithms for OTFS system

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IET COMMUNICATIONS 2022年第18期16卷 2158-2170页

作者： Ouchikh, Rabah Aissa-El-Bey, Abdeldjalil Chonavel, Thierry Djeddou, Mustapha Ecole Mil Polytech Lab Telecommun Bordj El Bahri Algeria IMT Atlantique UMR CNRS 6285 Lab STICC F-29238 Brest France

Orthogonal time-frequency space (OTFS) modulation, which has recently been proposed in the literature, is one of the promising techniques designed in the 2D Delay-Doppler domain adapted to combat high Doppler fading channels. However, channel estimation in high Doppler scenarios in advanced mobile-communication systems is still a challenging task. In this paper, the problem of channel estimation in the Delay-Doppler domain of the OTFS is focused on. First, a simple adaptation of the generalized orthogonal matching pursuit procedure, which will serve as a baseline method in this work, is proposed. Then, iterative algorithms are derived beneficiating from the sparsity of the channel. The unknown channel vector is separated into an unknown sparse support vector corresponding to the delay and Doppler taps, and an unknown vector of channel gains. These algorithms involve l(1)-norm minimization and a two-stage iterative procedure to recover alternatively the channel support and its coefficients. The estimation problem is also addressed from a Bayesian point of view. The sparse representation is reformulated as a specific marginalization of the maximum a posteriori problem on the support of the channel. To deal with the intractability of this problem, two existing techniques are adapted to this context, namely: The Monte Carlo Markov chain with the Gibbs sampler and variational mean-field approximation with the variational Bayesian expectation-maximization procedure. Finally, to assess the performance of the proposed algorithms, their complexity and performance are compared against existing methods. Experimental tests, conducted in high-mobility scenarios and low-latency applications, show that the proposed schemes are slightly more expensive in terms of complexity load but perform significantly better in terms of normalized mean square error and bit error rate.

关键词： Monte Carlo methods high-mobility scenarios sparse representation unknown channel vector fading channels two-stage iterative procedure mean square error methods Bayes methods generalized orthogonal matching pursuit procedure estimation problem maximum likelihood estimation high Doppler scenarios time-frequency analysis sparse channel estimation algorithms channel estimation advanced mobile-communication systems channel support orthogonal time-frequency space modulation minimisation error statistics high Doppler fading channels simple adaptation variational Bayesian expectation-maximization procedure iterative methods Delay-Doppler domain OTFS system posteriori problem unknown sparse support vector unknown vector channel gains expectation-maximisation algorithm Markov processes

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HANKEL-EM-SVD: a hybrid data dropout estimation technique for high voltage partial discharge signals

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2019年第6期13卷 824-835页

作者： Govindarajan, Suganya Subbaiah, Jayalalitha Krithivasan, Kannan Natarajan, Muthukumar SASTRA Deemed Be Univ Sch EEE Thanjavur Tamil Nadu India SASTRA Deemed Be Univ Tata Realty Srinivasa Ramanujam Res Cell Data Sci Lab Thanjavur India

Partial discharge (PD) measurement and characterisation is the most effective method to assess the insulation conditions of equipment for its diagnosis. A significant challenge in this area is PD denoising. Despite recent advancements in denoising algorithms, data drops out because of inappropriate threshold selection during denoising. Processing PD signals with missing data leads to an inaccurate assessment of the insulation conditions, indicating recovering such missed data is a potential research scope under the moonlight. This study addresses the issues of recovering such data dropouts by considering the PD data vector matrix as a low-rank matrix that needs completion by Hankel-matrix decomposition methods. After finding the probabilistic estimate of the missing values using an expectation maximisation algorithm, singular-value decomposition is used to find the actual missing values by soft-thresholding the singular values of the matrix. After testing this technique on simulated PD data, it is implemented to test SASTRA-High-Voltage Laboratory data, showing root mean square error (RMSE) 0.77% and mean absolute error 0.84%. The efficiency of this technique is confirmed when tested with large-sized noise free PD data of 36 samples from the Laboratory of BOLOGNA University (with RMSE varying from 0.15 to 0.31% and mean absolute error varying from 0.29 to 0.6%).

关键词： mean square error methods expectation-maximisation algorithm signal denoising partial discharge measurement partial discharges singular value decomposition HANKEL-EM-SVD high voltage partial discharge signals insulation conditions PD denoising inappropriate threshold selection inaccurate assessment missed data potential research scope PD-data vector-matrix low-rank matrix Hankel-matrix decomposition methods probabilistic estimate expectation maximisation algorithm singular-value decomposition actual missing values singular values simulated PD data SASTRA-High-Voltage Laboratory data root mean square mean absolute error hybrid data dropout estimation technique partial discharge measurement partial discharge characterisation PD signal processing BOLOGNA University

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