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Simultaneous Destriping and Image Denoising Using a Nonparametric Model With the em algorithm

IEEE TRANSACTIONS ON IMAGE PROCESSING 2023年 32卷 1065-1077页

作者： Song, Lingfei Huang, Hua Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Haihe Lab ITAI Tianjin 300450 Peoples R China Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

Digital images often suffer from the common problem of stripe noise due to the inconsistent bias of each column. The existence of the stripe poses much more difficulties on image denoising since it requires another n parameters, where n is the width of the image, to characterize the total interference of the observed image. This paper proposes a novel em-based framework for simultaneous stripe estimation and image denoising. The great benefit of the proposed framework is that it splits the overall destriping and denoising problem into two independent sub-problems, i.e., calculating the conditional expectation of the true image given the observation and the estimated stripe from the last round of iteration, and estimating the column means of the residual image, such that a Maximum Likelihood Estimation (MLE) is guaranteed and it does not require any explicit parametric modeling of image priors. The calculation of the conditional expectation is the key, here we choose a modified Non-Local Means algorithm to calculate the conditional expectation because it has been proven to be a consistent estimator under some conditions. Besides, if we relax the consistency requirement, the conditional expectation could be interpreted as a general image denoiser. Therefore other state-of-the-art image denoising algorithms have the potentials to be incorporated into the proposed framework. Extensive experiments have demonstrated the superior performance of the proposed algorithm and provide some promising results that motivate future research on the em-based destriping and denoising framework.

关键词： Noise reduction Maximum likelihood estimation Image denoising Approximation algorithms Signal processing algorithms Task analysis Noise measurement Stripe noise maximum likelihood estimation em algorithm conditional expectation

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Time series clustering with an em algorithm for mixtures of linear Gaussian state space models

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PATTERN RECOGNITION 2023年第1期138卷

作者： Umatani, Ryohei Imai, Takashi Kawamoto, Kaoru Kunimasa, Shutaro Shiga Univ Dept Data Sci Hikone Japan Shiga Univ Data Sci & AI Innovat Res Promot Ctr Hikone Japan Osaka Gas Co Ltd Business Anal Ctr Osaka Japan

In this paper, we consider the task of clustering a set of individual time series while modeling each cluster, that is, model-based time series clustering. The task requires a parametric model with sufficient flexibility to describe the dynamics in various time series. To address this problem, we propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models, which have high flexibility. The proposed method uses a new expectation-maximization algorithm for the mix-ture model to estimate the model parameters, and determines the number of clusters using the Bayesian information criterion. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection. The method is applied to real datasets commonly used to evaluate time series clustering methods. Results showed that the proposed method produces clustering results that are as accurate or more accurate than those obtained using previous methods.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： Time series clustering Model -based clustering State space model em algorithm Mixture model personal income patterns [5]

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Blind receiver design-based multi-dimensional stable distribution model using the em algorithm

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ADVANCES IN MECHANICAL ENGINEERING 2023年第8期15卷

作者： Ying, Wenwei Xiao, Jun Li, Chengjun Hong, Shaohua Naval Res Inst Beijing Peoples R China Underwater Acoust Antagonizing Lab Zhanjiang Peoples R China Xiamen Univ Sch Informat Xiamen Peoples R China Xiamen Univ Sch Informat 422Siming South Rd Xiamen 361005 Peoples R China

Multi-antenna receivers are a key technology for modern communication systems. Signal attenuation in the very low frequency (VLF) channel is a serious problem. In addition to high signal attenuation, the resulting noise on the VLF channel is non-Gaussian. Hence, to analyze and address the issue of the normal operation of the multi-antenna receiver in the VLF channel, we must study the signal detection problem in a multi-dimensional non-Gaussian fading channel. Motivated by the existing blind receiver in an underwater submarine single-antenna receiver, we study the signal detection and estimation algorithm under the fading channel for a multi-dimensional non-Gaussian noise model. In this study, we propose a blind receiver based on the expectation-maximization (em) algorithm. The proposed blind receiver can reduce non-Gaussian noise. In addition, we propose a nonlinear receiver that can accurately receive the transmitted signal over the high-attenuation VLF communication systems. Numerical and simulation results over uncorrelated and correlated non-Gaussian channels confirm that the design of the proposed blind receiver is close to optimal, with low computation complexity.

关键词： Blind receiver em algorithm non-Gaussian noise model parameter estimation VLF communication systems

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Integrating the em algorithm with particle filter for image restoration with exponential dispersion noise

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2023年第2期52卷 446-462页

作者： Sadok, Ibrahim Masmoudi, Afif Zribi, Mourad Univ Sfax Sfax Fac Sci Lab Probabil & Stat BP 1171 Sfax 3038 Tunisia ULCO Lab Informat Signal & Image Cote Opale LISIC EA 4 Calais France

Images are often degraded during the data acquisition process. The degradation may involve blurring, information loss due to sampling, and various sources of noise. The purpose of image restoration is to estimate the original image from the degraded data. The present work sets forward a restoration technique for exponential dispersion noise based on Particle filtering (PF) using Hidden Markov Model. In order not to take observation information into account in general, the PF algorithm produced an incorrect sample from a discrete approximation distribution. To resolve this problem, we propose in the resampling stage of PF, samples which are generated from a continuous distribution rather than a discrete one based on Exponential Dispersion Models (EDM). An iterative approach, called the Expectation-Maximization (em) algorithm, is used to find the maximum likelihood estimates of the relevant unknown parameters of the EDM. Moreover, under some conditions, the concavity of the conditional expected log-likelihood function is established in the maximization step of the em algorithm. The proposed approach is rooted in ideas from statistics, control theory and signal processing. Experimental results are eventually displayed with simulation and satellite images, which demonstrate the good performance of the proposed approach.

关键词： Image restoration Hidden Markov Model exponential dispersion distribution particle filtering em algorithm noise

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Estimation of the time of exposure based on interval and censored data using the e-accelerated em algorithm

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STATISTICS IN MEDICINE 2023年第25期42卷 4542-4555页

作者： Yoneoka, Daisuke Kawashima, Takayuki Tanoue, Yuta Nomura, Shuhei Eguchi, Akifumi Natl Inst Infect Dis Ctr Surveillance Immunizat & Epidemiol Res Tokyo Japan Tokyo Inst Technol Dept Math & Comp Sci Tokyo Japan Tokyo Univ Marine Sci & Technol Fac Marine Technol Tokyo Japan Keio Univ Sch Med Tokyo Japan Chiba Univ Ctr Prevent Med Sci Chiba Japan 1-23-1 Toyama Shinju Ku Tokyo 1628640 Japan

Accurately estimating the timing of pathogen exposure plays a crucial role in outbreak control for emerging infectious diseases, including the source identification, contact tracing, and vaccine research and development. However, since surveillance activities often collect data retrospectively after symptoms have appeared, obtaining accurate data on the timing of disease onset is difficult in practice and can involve "coarse" observations, such as interval or censored data. To address this challenge, we propose a novel likelihood function, tailored to coarsely observed data in rapid outbreak surveillance, along with an optimization method based on an e-accelerated em algorithm for faster convergence to find maximum likelihood estimates (MLEs). The covariance matrix of MLEs is also discussed using a nonparametric bootstrap approach. In terms of bias and mean-squared error, the performance of our proposed method is evaluated through extensive numerical experiments, as well as its application to a series of epidemiological surveillance focused on cases of mass food poisoning. The experiments show that our method exhibits less bias than conventional methods, providing greater efficiency across all scenarios.

关键词： e-acceleration em algorithm exposure time infectious disease three-parameter lognormal

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Blind Residual CFO Estimation via CNN-Enabled em algorithm 99

Blind Residual CFO Estimation via CNN-Enabled EM Algorithm

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IEEE 99th Vehicular Technology Conference (VTC-Spring)

作者： Cai, Penghao Yang, Fuqian Xue, Zhipeng Wang, Yutao Zhu, Hanyu Gao, Xiqi Purple Mt Labs Nanjing 211100 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

ISBN: (纸本)9798350387414

Large residual carrier frequency offset (CFO) can severely degrade the performance of orthogonal frequency division multiplexing (OFDM) wireless communication systems when high-order modulations are adopted. In this paper, we propose a convolution neural network (CNN) enabled expectation-maximization (em) algorithm which can blindly estimate residual CFO without extra pilots. Specifically, we first show that the effects of the residual CFO can be depicted by the phase shift existing in the equalized signal. Based on this model, we design a simple CNN to get a rough estimate of the phase shift. The output of the CNN is further used to initialize an em algorithm. With this fine initialization, the em algorithm can iteratively seek better estimates of the phase shift induced by the residual CFO. The combination of CNN and em algorithm simplifies neural network design while maintaining the accuracy of the estimation. Numerical simulations verify the efficiency of the proposed method.

关键词： OFDM residue carrier frequency offset (CFO) blind CFO estimation em algorithm CNN synchronization

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General em algorithm for fitting non-monotone hazard functions from truncated and censored observations

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OPERATIONS RESEARCH LETTERS 2022年第5期50卷 476-483页

作者： Barde, Stephane Ko, Young Myoung Shin, Hayong Samsung Elect Innovat Ctr Hwasung 18448 Gyeonggi South Korea Korea Adv Inst Sci & Technol Dept Ind & Syst Engn 291 Daehak Ro Daejeon 34141 South Korea Pohang Univ Sci & Technol Dept Ind & Management Engn Pohang 37673 Gyeongbuk South Korea

Recently, many researchers focused on modeling non-monotonic hazard functions such as bath-tube and hump shapes. However, most of their estimation methods are focused on complete observations. Since reliability data are typically censored and truncated, a general em algorithm is proposed, which can fit any of those complex hazard functions. The proposed em algorithm is analyzed by fitting well-known 4-parameter hazard functions, where its performance is compared by their specific direct methods through extensive Monte Carlo simulations. (c) 2022 Elsevier B.V. All rights reserved.

关键词： em algorithm Sequential quadratic programming Phase-type distribution Left-truncated & right-censored Hazard function

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Beyond the em algorithm: constrained optimization methods for latent class model

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第9期51卷 5222-5244页

作者： Chen, Hao Han, Lanshan Lim, Alvin Precima Res & Dev Chicago IL 60631 USA

Latent class model (LCM), which is a finite mixture of different categorical distributions, is one of the most widely used models in statistics and machine learning fields. Because of its noncontinuous nature and flexibility in shape, researchers in areas such as marketing and social sciences also frequently use LCM to gain insights from their data. One likelihood-based method, the expectation-maximization (em) algorithm, is often used to obtain the model estimators. However, the em algorithm is well-known for its notoriously slow convergence. In this research, we explore alternative likelihood-based methods that can potential remedy the slow convergence of the em algorithm. More specifically, we regard likelihood-based approach as a constrained nonlinear optimization problem, and apply quasi-Newton type methods to solve them. We examine two different constrained optimization methods to maximize the log-likelihood function. We present simulation study results to show that the proposed methods not only converge in less iterations than the em algorithm but also produce more accurate model estimators.

关键词： Constrained optimization em algorithm Finite mixture model Latent class model Quasi-Newton's method Quadratic programing

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An em algorithm for analyzing right-censored survival data under the semiparametric proportional odds model

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COMMUNICATIONS IN STATISTICS-THEORY AND METHODS 2022年第15期51卷 5284-5297页

作者： Wang, Lu Wang, Lianming Univ South Carolina Dept Stat 1523 Greene St Columbia SC 29208 USA

The semiparametric proportional odds (PO) model is a popular alternative to Cox's proportional hazards model for analyzing survival data. Although many approaches have been proposed for this topic in the literature, most of the existing approaches have been found computationally expensive and difficult to implement. In this article, a novel and easy-to-implement approach based on an expectation-maximization (em) algorithm is proposed for analyzing right-censored data. The em algorithm involves only solving a low-dimensional estimating equation for the regression parameters and then updating the spline coefficients in simple closed form at each iteration. Our method is robust to initial values, converges fast, and provides the variance estimates in closed form. Simulation studies suggest that the proposed method has excellent performance in estimating both regression parameters and the baseline survival function, even when the right censoring rate is very high. The method is applied to a large dataset about breast cancer survival extracted from the Surveillance, Epidemiology, and End Results (SEER) database maintained by the U.S. National Cancer Institute. This method is now available in R package regPOr for public use.

关键词： Semiparametric regression em algorithm monotone splines proportional odds model survival analysis Survival analysis and censored data Estimation in survival analysis and censored data)

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A fast approximate em algorithm for joint models of survival and multivariate longitudinal data

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2022年 170卷 107438-107438页

作者： Murray, James Philipson, Pete Newcastle Univ Sch Math Stat & Phys Newcastle upon Tyne England

Joint models are an increasingly popular way to characterise the relationship between one or more longitudinal responses and an event of interest. However, for multivariate joint models the increased dimensionality and complexity of random effects present in the model specification are commensurate with increased computing time, hampering the implementation of many classic approaches. An approximate em algorithm which ameliorates the so-called 'curse of dimensionality' is developed. The scaleability and accuracy of the proposed method are demonstrated via two simulation studies and applied to data arising from two clinical trials in the disease areas of cirrhosis and Alzheimer's disease, each with three biomarkers. (C)& nbsp;2022 The Author(s). Published by Elsevier B.V.

关键词： Joint models em algorithm Multivariate normal Simulation Multiple longitudinal

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