检索结果-内蒙古大学图书馆

Adjacency-based regularization for partially ranked data with non-ignorable missing

COMPUTATIONAL STATISTICS & DATA ANALYSIS 2020年第0期145卷 106905-000页

作者： Nakamura, Kento Yano, Keisuke Komaki, Fumiyasu Univ Tokyo Grad Sch Informat Sci & Technol Dept Math Informat Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan RIKEN Ctr Brain Sci 2-1 Hirosawa Wako Saitama 3510198 Japan

In analyzing ranked data, we often encounter situations in which data are partially ranked. Regarding partially ranked data as missing data, this paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing mechanism. We propose estimators for both complete rankings and missing mechanisms together with a simple estimation procedure. The proposed procedure leverages the structured regularization based on an adjacency structure behind partially ranked data as well as the expectation-maximization algorithm. The experimental results demonstrate that the proposed estimator works well under non-ignorable missing mechanisms. Published by Elsevier B.V.

关键词： Alternating Direction Method of Multipliers expectation-maximization algorithms Kendall distances Mallows models

来源：评论

学校读者我要写书评

暂无评论

An Accurate, Fast Approximation for the Sum of Fading Random Variables via expectation maximization Applications to Diversity Systems

引用

IEEE ACCESS 2018年 6卷 42616-42630页

作者： Vega Sanchez, Jose David Urquiza-Aguiar, Luis Paredes Paredes, Martha Cecilia Escuela Politec Nacl Dept Elect Telecomunicac & Redes Informac Fac Elect & Elect Quito 170525 Ecuador

Sums of fading envelopes occur in several wireless communications applications. The exact mathematical solution to this statistic is, however, rather intricate. In this paper, we derive a novel closed-form approximation to the sum of not necessarily identically distributed Nakagami-m random variables. The necessary parameters of the approximate solution are estimated by using the well-known expectation maximization algorithm with a Nakagami-m mixture model. The proposed approximation finds applicability in obtaining important performance metrics of communications systems where sums of variates arise. More specifically, we apply the proposed method to derive a closed-form expression for average bit error probability (ABEP) of multibranch equal-gain combining receivers. The presented models are general and can be applied to any modulation scheme. Furthermore, simplified asymptotic closed-form expressions for the ABEP have been derived to examine the achievable diversity and coding gains. Finally, the performance of the proposed approach is verified by comparing itself against both the exact evaluation and the previous results in the literature.

关键词： Approximation methods diversity systems expectation-maximization algorithms Nakagami-m distribution performance analysis

来源：评论

学校读者我要写书评

暂无评论

Parameter Estimation For Bursty-Intermittent Observations

Parameter Estimation For Bursty-Intermittent Observations

引用

IEEE Signal Processing and Communications Applications (SIU)

作者： Çagatay Candan Elektrik ve Elektronik Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi

ISBN: (数字)9781728172064

ISBN: (纸本)9781728172071

Parameter estimation problem is examined in the setting where the noise power is allowed to change from sample to sample. Parameters of the noise source is assumed to be generated by a Markov chain whose state sequence is not known by the observation system. expectation-maximization algorithm is applied for the estimation of desired parameter with the inclusion of unknown state vector of the Markov chain realization as a latent variable. The suggested scheme can be utilized in applications with bursty noise and/or intermittent signals.

关键词： Signal to noise ratio Markov processes Hidden Markov models Kalman filters Estimation Parameter estimation expectation-maximization algorithms

来源：评论

学校读者我要写书评

暂无评论

Solving Time-Domain Audio Inverse Problem Using Nonnegative Tensor Factorization

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第21期66卷 5604-5617页

作者： Bilen, Cagdas Ozerov, Alexey Perez, Patrick Audio Analyt Ltd Cambridge CB5 8AB England Technicolor F-35576 Cesson Sevigne France Valeo Ai F-75017 Paris France

Nonnegative matrix factorization (NMF) and nonnegative tensor factorization (NTF) are important tools for modeling nonnegative data, which gained increasing popularity in various fields, a significant one of which is audio processing. However, there are still many problems in audio processing, for which the NMF (or NTF) model has not been successfully utilized. In this paper, we propose a new algorithm based on the NMF (and NTF) in the short-time Fourier domain for solving a large class of audio inverse problems with missing or corrupted time-domain samples. The proposed approach overcomes the difficulty of employing a model in the frequency domain to recover time-domain samples with the help of probabilistic modeling. Its performance is demonstrated for the following applications: audio declipping and declicking (never solved with NMF/NTF modeling prior to this paper);joint audio declipping/declicking and source separation (never solved with NMF/NTF modeling or any other method prior to this paper);and compressive sampling recovery and compressive sampling-based informed source separation (an extremely low complexity encoding scheme that is possible with the proposed approach and has never been proposed prior to this paper).

关键词： Signal reconstruction signal restoration maximum likelihood estimation maximum a posteriori estimation expectation-maximization algorithms iterative algorithms

来源：评论

学校读者我要写书评

暂无评论

A GAMP-Based Low Complexity Sparse Bayesian Learning Algorithm

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第2期66卷 294-308页

作者： Al-Shoukairi, Maher Schniter, Philip Rao, Bhaskar D. Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

In this paper, we present an algorithm for the sparse signal recovery problem that incorporates damped Gaussian generalized approximate message passing (GGAMP) into expectation-maximization-based sparse Bayesian learning (SBL). In particular, GGAMP is used to implement the E-step in SBL in place of matrix inversion, leveraging the fact that GGAMP is guaranteed to converge with appropriate damping. The resulting GGAMP-SBL algorithm is much more robust to arbitrary measurement matrix A than the standard damped GAMP algorithm while being much lower complexity than the standard SBL algorithm. We then extend the approach from the single measurement vector case to the temporally correlated multiple measurement vector case, leading to the GGAMP-TSBL algorithm. We verify the robustness and computational advantages of the proposed algorithms through numerical experiments.

关键词： Compressed sensing approximate message passing (AMP) sparse Bayesian learning (SBL) expectation-maximization algorithms Gaussian scale mixture multiple measurement vectors (MMV)

来源：评论

学校读者我要写书评

暂无评论

Mixture Semisupervised Bayesian Principal Component Regression for Soft Sensor Modeling

引用

IEEE ACCESS 2018年 6卷 40909-40919页

作者： Zhu, Pengbo Liu, Xin Wang, Yanbo Yang, Xianqiang Harbin Inst Technol Res Inst Intelligent Control & Syst Harbin 150080 Heilongjiang Peoples R China

In this paper, a mixture semisupervised Bayesian principal component regression-based soft sensor modeling method for nonlinear industrial process with multiple operating modes is presented. In many chemistry processes, part of output data samples may be unavailable due to the difficulties in measurement or recording. The semisupervised method is introduced to efficiently deal with the unlabeled data set. Moreover, the Bayesian regularization method is proposed to determine the unknown dimensionality of latent variables space in each submode by introducing three different formulations of two hyperparameters to construct the Gaussian prior distributions over the loading and regression matrices. The formulation of this method is derived in expectation maximization algorithm scheme, and the formulas to update unknown parameters are derived. The effectiveness of the proposed method is verified through a numerical example, the Tennessee Eastman benchmark process, and the comparisons with the existing method.

关键词： Principal component analysis semisupervised learning mixture models expectation-maximization algorithms Bayesian regularization

来源：评论

学校读者我要写书评

暂无评论

A Framework for Clustered and Skewed Sparse Signal Recovery

引用

IEEE TRANSACTIONS ON SIGNAL PROCESSING 2018年第15期66卷 3972-3986页

作者： Wang, Sheng Rahnavard, Nazanin Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

A novel framework, clustered-skew normal mixture-belief propagation, is developed to solve the reconstruction of undersampled clustered signals, where the magnitudes of signal coefficients in each cluster are distributed asymmetrically w.r.t the cluster mean. To address the skewness feature, a finite skew-normal density mixture is utilized to model the prior distribution, where the marginal posterior of the signal is inferred by an efficient approximate message-passing-based algorithm. An expectation-maximization-based algorithm is developed to estimate the mixture density. The clustered property is then modeled by the Potts model, and a loopy belief propagation algorithm is designed to promote the spatial feature. Experimental results show that our technique is highly effective and efficient in exploiting both the clustered feature and asymmetrical feature of the signals and outperforms many sophisticated techniques.

关键词： Compressed sensing asymmetrical signal approximate message passing expectation-maximization algorithms

来源：评论

学校读者我要写书评

暂无评论

ON THE IMPLEMENTATION AND ANALYSIS OF expectation maximization algorithms WITH STOPPING CRITERIONmaximization

ON THE IMPLEMENTATION AND ANALYSIS OF EXPECTATION MAXIMIZATI...

引用

19th IEEE International Conference on Image Processing (ICIP)

作者： Ryan, Andrew Mora, Benjamin Chen, Min Swansea Univ Coll Sci Dept Comp Sci Swansea W Glam Wales Univ Oxford Oxford E Res Ctr Oxford England

ISBN: (纸本)9781467325332;9781467325349

The expectation maximization (EM) algorithm is an alternative reconstruction method to the Filtered Back Projection method, providing many advantages including decreased sensitivity to noise. However the algorithm requires a large number of iterations to reach adequate convergence. Due to this, research has been carried out into accelerating the convergence rate of the EM algorithm. In this paper we present an analysis of an EM implementation which uses both OSEM and MGEM, comparing results on a per time basis with both acceleration techniques alone as well as a combination of the two methods. We provide an alternative stopping criterion based on the RMS error of the projections of the current reconstruction and compare the result with an existing variance based approach.

关键词： Reconstruction algorithms Computed tomography expectation-maximization algorithms Mean square error methods Acceleration techniques

来源：评论

学校读者我要写书评

暂无评论

Performance Comparison of Recent Imputation Methods for Classification Tasks over Binary Data

引用

APPLIED ARTIFICIAL INTELLIGENCE 2017年第1期31卷 1-22页

作者： Ghorbani, Soroosh Desmarais, Michel C. Polytech Montreal Dept Comp Engn 2900 Boul Edouard Montpetit Montreal PQ H3T 1J4 Canada

This paper evaluates the effect on the predictive accuracy of different models of two recently proposed imputation methods, namely missForest (MF) and Multiple Imputation based on expectation-maximization (MIEM), along with two other imputation methods: Sequential Hot-deck and Multiple Imputation based on Logistic Regression (MILR). Their effect is assessed over the classification accuracy of four different models, namely Tree Augmented Naive Bayes (TAN) which has received little attention, Naive Bayes (NB), Logistic Regression (LR), and Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. Experiments are conducted over fourteen binary datasets with large feature sets, and across a wide range of missing data rates (between 5 and 50%). The results from 10 fold cross-validations show that the performance of the imputation methods varies substantially between different classifiers and at different rates of missing values. The MIEM method is shown to generally give the best results for all the classifiers across all rates of missing data. While NB model does not benefit much from imputation compared to a no imputation baseline, LR and TAN are highly susceptible to gain from the imputation methods at higher rates of missing values. The results also show that MF works best with TAN, and Hot-deck degrades the predictive performance of SVM and NB models at high rates of missing values (over 30%). Detailed analysis of the imputation methods over the different datasets is reported. Implications of these findings on the choice of an imputation method are discussed.

关键词： expectation-maximization algorithms Logistic regression analysis Naive Bayes classification Support vector machines Radial basis functions

来源：评论

学校读者我要写书评

暂无评论

Stochastic degradation process modeling and remaining useful life estimation with flexible random-effects

引用

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS 2017年第6期354卷 2477-2499页

作者： Zhang, Zhengxin Hu, Changhua Si, Xiaosheng Zhang, Jianxun Zheng, Jianfei Xian Inst High Tech Xian 710025 Shaanxi Provinc Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Models with random-effects are generally used in the field of degradation modeling and remaining useful life (RUL) estimation for describing unit-to-unit variability. The wide employment of parameters, which is assumed to be subjected to normal distribution to capture this variability, may disaccord with actual industrial conditions, and will introduce misspecifications. Such misspecification can affect the accuracy of RUL estimation and the subsequent inference results. In this paper, we propose a degradation model with flexible random-effects, which makes it flexible to choose distributions to portray the unit-to-unit variability according to the available information. To do so, the mixture of normal distributions, as a distribution describing random-effects, is incorporated into a class of diffusion process based degradation models whose drift coefficient is a linear combination of some time-dependent functions with known forms. The combination coefficients of each function are treated as random variables drawn from the mixture of normal distributions. An analytical approximated probability density function (PDF) of the RUL is derived under the concept of first passage time (FPT). To identify the model parameters, a framework for parameter estimation is presented based on stochastic expectation maximization (SEM) algorithm. Finally, simulation studies are provided to demonstrate the superiority of the normal mixture over the individual normal distribution for describing random effects in RUL estimation. (C) 2016 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

关键词： STOCHASTIC models RANDOM effects model DIFFUSION processes PROBABILITY density function expectation-maximization algorithms PARAMETER estimation

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：