EM Algorithm Based on Fuzzification and its Application

2012 ◽  
Vol 532-533 ◽  
pp. 1445-1449
Author(s):  
Ting Ting Tong ◽  
Zhen Hua Wu
Keyword(s):  
Em Algorithm ◽  
Mixed Model ◽  
Image Data ◽  
Training Sample ◽  
Model Parameters ◽  
Parameter Learning ◽  
Weighted Degree ◽  
The Em Algorithm ◽  
The Impact

EM algorithm is a common method to solve mixed model parameters in statistical classification of remote sensing image. The EM algorithm based on fuzzification is presented in this paper to use a fuzzy set to represent each training sample. Via the weighted degree of membership, different samples will be of different effect during iteration to decrease the impact of noise on parameter learning and to increase the convergence rate of algorithm. The function and accuracy of classification of image data can be completed preferably.

scholarly journals Maximum Likelihood Estimation using the EM Algorithm

2021 ◽  
Vol 8 (9) ◽  
pp. 275-277
Author(s):  
Ahsene Lanani
Keyword(s):  
Missing Data ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Em Algorithm ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Likelihood Estimation ◽  
Mixed Linear Model ◽  
Model Parameters ◽  
The Em Algorithm

This paper yields with the Maximum likelihood estimation using the EM algorithm. This algorithm is very used to solve nonlinear equations with missing data. We estimated the linear mixed model parameters and those of the variance-covariance matrix. The considered structure of this matrix is not necessarily linear. Keywords: Algorithm EM; Maximum likelihood; Mixed linear model.

scholarly journals Unsupervised Learning in RSS-Based DFLT Using an EM Algorithm

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5549
Author(s):  
Ossi Kaltiokallio ◽  
Roland Hostettler ◽  
Hüseyin Yiğitler ◽  
Mikko Valkama
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Tracking System ◽  
Model Parameters ◽  
Calibration Data ◽  
Tracking Accuracy ◽  
Time Period ◽  
The Em Algorithm ◽  
Device Free ◽  
Localization And Tracking

Received signal strength (RSS) changes of static wireless nodes can be used for device-free localization and tracking (DFLT). Most RSS-based DFLT systems require access to calibration data, either RSS measurements from a time period when the area was not occupied by people, or measurements while a person stands in known locations. Such calibration periods can be very expensive in terms of time and effort, making system deployment and maintenance challenging. This paper develops an Expectation-Maximization (EM) algorithm based on Gaussian smoothing for estimating the unknown RSS model parameters, liberating the system from supervised training and calibration periods. To fully use the EM algorithm’s potential, a novel localization-and-tracking system is presented to estimate a target’s arbitrary trajectory. To demonstrate the effectiveness of the proposed approach, it is shown that: (i) the system requires no calibration period; (ii) the EM algorithm improves the accuracy of existing DFLT methods; (iii) it is computationally very efficient; and (iv) the system outperforms a state-of-the-art adaptive DFLT system in terms of tracking accuracy.

scholarly journals The Orthogonally Partitioned EM Algorithm: Extending the EM Algorithm for Algorithmic Stability and Bias Correction Due to Imperfect Data

2016 ◽  
Vol 12 (1) ◽  
pp. 65-77
Author(s):  
Michael D. Regier ◽  
Erica E. M. Moodie
Keyword(s):  
Missing Data ◽  
Measurement Error ◽  
Em Algorithm ◽  
Optimal Solution ◽  
Bias Reduction ◽  
Model Parameters ◽  
Finite Sample ◽  
Imperfect Data ◽  
The Em Algorithm ◽  
Finite Sample Properties

Abstract We propose an extension of the EM algorithm that exploits the common assumption of unique parameterization, corrects for biases due to missing data and measurement error, converges for the specified model when standard implementation of the EM algorithm has a low probability of convergence, and reduces a potentially complex algorithm into a sequence of smaller, simpler, self-contained EM algorithms. We use the theory surrounding the EM algorithm to derive the theoretical results of our proposal, showing that an optimal solution over the parameter space is obtained. A simulation study is used to explore the finite sample properties of the proposed extension when there is missing data and measurement error. We observe that partitioning the EM algorithm into simpler steps may provide better bias reduction in the estimation of model parameters. The ability to breakdown a complicated problem in to a series of simpler, more accessible problems will permit a broader implementation of the EM algorithm, permit the use of software packages that now implement and/or automate the EM algorithm, and make the EM algorithm more accessible to a wider and more general audience.

scholarly journals Hybrid DE-EM Algorithm for Gaussian Mixture Model-Based Wireless Channel Multipath Clustering

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Yupeng Li ◽  
Jianhua Zhang ◽  
Ruisi He ◽  
Lei Tian ◽  
Hewen Wei
Keyword(s):  
Em Algorithm ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Clustering Algorithm ◽  
Gaussian Mixture ◽  
Wireless Channel ◽  
Model Parameters ◽  
Em Algorithms ◽  
The Em Algorithm ◽  
Searching Ability

In this paper, the Gaussian mixture model (GMM) is introduced to the channel multipath clustering. In the GMM field, the expectation-maximization (EM) algorithm is usually utilized to estimate the model parameters. However, the EM widely converges into local optimization. To address this issue, a hybrid differential evolution (DE) and EM (DE-EM) algorithms are proposed in this paper. To be specific, the DE is employed to initialize the GMM parameters. Then, the parameters are estimated with the EM algorithm. Thanks to the global searching ability of DE, the proposed hybrid DE-EM algorithm is more likely to obtain the global optimization. Simulations demonstrate that our proposed DE-EM clustering algorithm can significantly improve the clustering performance.

scholarly journals Thinning Effects on the Tree Height–Diameter Allometry of Masson Pine (Pinus massoniana Lamb.)

Forests ◽  
2019 ◽  
Vol 10 (12) ◽  
pp. 1129 ◽  
Author(s):  
Cheng Deng ◽  
Shougong Zhang ◽  
Yuanchang Lu ◽  
Robert E. Froese ◽  
Angang Ming ◽  
...  
Keyword(s):  
Mixed Model ◽  
Sustainable Forest Management ◽  
Tree Height ◽  
Model Parameters ◽  
Diameter Growth ◽  
Growth Data ◽  
Allometric Relationship ◽  
Masson Pine ◽  
Thinning Intensity ◽  
The Impact

The stem height–diameter allometric relationship is fundamental in determining forest and ecosystem structures as well as in estimating tree volume, biomass, and carbon stocks. Understanding the effects of silvicultural practices on tree height–diameter allometry is necessary for sustainable forest management, though the impact of measures such as thinning on the allometric relationship remain understudied. In the present study, the effects of thinning on tree height–diameter allometry were evaluated using Masson pine height and diameter growth data from a plantation experiment that included unthinned and thinned treatments with different intensities. To determine whether thinning altered the height–diameter allometry rhythm, the optimal height–diameter model was identified and dummy variable methods were used to investigate the differences among model parameters for different thinning treatments. Periodic (annual) allometric coefficients were calculated based on height and diameter increment data and were modeled using the generalized additive mixed model (GAMM) to further illustrate the response of tree height–diameter allometry to different thinning treatments over time. Significant differences were detected among the parameters of the optimal height–diameter model (power function) for different thinning treatments, which indicated that the pattern of the height–diameter allometry relationship of Masson pine was indeed altered by thinning treatments. Results also indicated a nonlinear trend in the allometric relationship through time which was significantly affected by thinning. The height–diameter allometric coefficient exhibited a unimodal convex bell curve with time in unthinned plots, and thinning significantly interfered with the original trend of the height–diameter allometric coefficient. Thinning caused trees to increase diameter growth at the expense of height growth, resulting in a decrease of the ratio of tree height to diameter, and this trend was more obvious as the thinning intensity increased.

scholarly journals StoolNet for Color Classification of Stool Medical Images

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1464 ◽  
Author(s):  
Ziyuan Yang ◽  
Lu Leng ◽  
Byung-Gyu Kim
Keyword(s):  
Medical Images ◽  
Low Cost ◽  
Automatic Segmentation ◽  
Region Of Interest ◽  
Training Sample ◽  
Color Classification ◽  
Processing Technologies ◽  
Heavy Burden ◽  
The Impact

The color classification of stool medical images is commonly used to diagnose digestive system diseases, so it is important in clinical examination. In order to reduce laboratorians’ heavy burden, advanced digital image processing technologies and deep learning methods are employed for the automatic color classification of stool images in this paper. The region of interest (ROI) is segmented automatically and then classified with a shallow convolutional neural network (CNN) dubbed StoolNet. Thanks to its shallow structure and accurate segmentation, StoolNet can converge quickly. The sufficient experiments confirm the good performance of StoolNet and the impact of the different training sample numbers on StoolNet. The proposed method has several advantages, such as low cost, accurate automatic segmentation, and color classification. Therefore, it can be widely used in artificial intelligence (AI) healthcare.

scholarly journals Two-part quantile regression models for semi-continuous longitudinal data: A finite mixture approach

2021 ◽  
pp. 1471082X2199360
Author(s):  
Luca Merlo ◽  
Antonello Maruotti ◽  
Lea Petrella
Keyword(s):  
Longitudinal Data ◽  
Em Algorithm ◽  
Quantile Regression ◽  
Finite Mixture ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Quantile Regression Model ◽  
The Em Algorithm ◽  
Binary Response Variable ◽  
Rand Health Insurance Experiment

This article develops a two-part finite mixture quantile regression model for semi-continuous longitudinal data. The proposed methodology allows heterogeneity sources that influence the model for the binary response variable to also influence the distribution of the positive outcomes. As is common in the quantile regression literature, estimation and inference on the model parameters are based on the asymmetric Laplace distribution. Maximum likelihood estimates are obtained through the EM algorithm without parametric assumptions on the random effects distribution. In addition, a penalized version of the EM algorithm is presented to tackle the problem of variable selection. The proposed statistical method is applied to the well-known RAND Health Insurance Experiment dataset which gives further insights on its empirical behaviour.

scholarly journals Modeling in Forestry Using Mixture Models Fitted to Grouped and Ungrouped Data

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1196
Author(s):  
Eric K. Zenner ◽  
Mahdi Teimouri
Keyword(s):  
Em Algorithm ◽  
Mixture Models ◽  
Goodness Of Fit ◽  
Diameter Distribution ◽  
Model Parameters ◽  
Mixture Distributions ◽  
Component Mixture ◽  
Nk Model ◽  
The Em Algorithm ◽  
Log Normal

The creation and maintenance of complex forest structures has become an important forestry objective. Complex forest structures, often expressed in multimodal shapes of tree size/diameter (DBH) distributions, are challenging to model. Mixture probability density functions of two- or three-component gamma, log-normal, and Weibull mixture models offer a solution and can additionally provide insights into forest dynamics. Model parameters can be efficiently estimated with the maximum likelihood (ML) approach using iterative methods such as the Newton-Raphson (NR) algorithm. However, the NR algorithm is sensitive to the choice of initial values and does not always converge. As an alternative, we explored the use of the iterative expectation-maximization (EM) algorithm for estimating parameters of the aforementioned mixture models because it always converges to ML estimators. Since forestry data frequently occur both in grouped (classified) and ungrouped (raw) forms, the EM algorithm was applied to explore the goodness-of-fit of the gamma, log-normal, and Weibull mixture distributions in three sample plots that exhibited irregular, multimodal, highly skewed, and heavy-tailed DBH distributions where some size classes were empty. The EM-based goodness-of-fit was further compared against a nonparametric kernel-based density estimation (NK) model and the recently popularized gamma-shaped mixture (GSM) models using the ungrouped data. In this example application, the EM algorithm provided well-fitting two- or three-component mixture models for all three model families. The number of components of the best-fitting models differed among the three sample plots (but not among model families) and the mixture models of the log-normal and gamma families provided a better fit than the Weibull distribution for grouped and ungrouped data. For ungrouped data, both log-normal and gamma mixture distributions outperformed the GSM model and, with the exception of the multimodal diameter distribution, also the NK model. The EM algorithm appears to be a promising tool for modeling complex forest structures.

scholarly journals Scene classification of remote sensing image based on compound pruning

2021 ◽  
Vol 336 ◽  
pp. 06030
Author(s):  
Fengbing Jiang ◽  
Fang Li ◽  
Guoliang Yang
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Data ◽  
Remote Sensing Image ◽  
Convolution Neural Network ◽  
Model Parameters ◽  
Scene Classification ◽  
Remote Sensing Image Classification ◽  
And Storage

Convolution neural network for remote sensing image scene classification consumes a lot of time and storage space to train, test and save the model. In this paper, firstly, elastic variables are defined for convolution layer filter, and combined with filter elasticity and batch normalization scaling factor, a compound pruning method of convolution neural network is proposed. Only the superparameter of pruning rate needs to be adjusted during training. in the process of training, the performance of the model can be improved by means of transfer learning. In this paper, algorithm tests are carried out on NWPU-RESISC45 remote sensing image data to verify the effectiveness of the proposed method. According to the experimental results, the proposed method can not only effectively reduce the number of model parameters and computation, but also ensure the accuracy of the algorithm in remote sensing image classification.

scholarly journals Spatial Modelling of Covid-19 Confirmed Cases in Kalimantan, Indonesia: How Neighborhood Matters?

2021 ◽  
Vol 18 (15) ◽  
Author(s):  
Muhammad Luthfi Setiarno PUTERA ◽  
Laili WAHYUNITA ◽  
Febrianawati YUSUP
Keyword(s):  
Mixed Model ◽  
Negative Binomial ◽  
Early Stage ◽  
Negative Binomial Regression ◽  
Kernel Weight ◽  
Health Care Facilities ◽  
Health Determinants ◽  
Model Parameters ◽  
Binomial Regression ◽  
The Impact

The Covid-19 outbreak has hit all countries across the globe, including Indonesia, in which the impact is detrimental and costly. We investigated 14 determinants that could spatially influence Covid-19 cases in Central Kalimantan and South Kalimantan provinces in mid-2020 by using the Geographically Weighted Negative Binomial Regression (GWNBR) and Mixed Geographically Weighted Negative Binomial Regression (MGWNBR). This study conducted iterative Limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundaries (L-BFGS-B) to utilize the numerical parameter estimation of MGWNBR. MGWNBR identified that the adjacent regions tend to group in 8 clusters containing the same significant determinants. Through MGWNBR, the comorbid prevalences (acute respiratory infection, pneumonia, and diabetes) were positively associated with the Covid-19 increasing cases in most regions. The unemployment rate and the number of health care facilities were negatively related to the increase of Covid-19 cases in some regions. MGWNBR was better than GWNBR in terms of AIC, deviance, and pseudo R-sq. The residual map also suggested that MGWNBR produced a more accurate projection than GWNBR. HIGHLIGHTS The statistical models for two Kalimantan provinces of Indonesia during early stage of Covid-19 pandemic Those models consisting of global, local, and mixed models estimated the effect of various social, economical, and health determinants on Covid-19 cases Mixed model parameters are estimated iteratively using L-BFGS-B weighted by adaptive bisquare kernel weight Comparison of models’ performance are applied using deviance, AIC, and pseudo R-sq GRAPHICAL ABSTRACT

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