scholarly journals Clustering and Classification in Fisheries

2021 ◽  
Author(s):  
◽  
Yuki Fujita
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Finite Mixture Models ◽  
Real Life ◽  
Point Of View ◽  
Atmospheric Research ◽  
The Em Algorithm ◽  
Clustering And Classification ◽  
First Time ◽  
Trawl Surveys

<p>This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New Zealand waters. In particular we use fish abundance indices on the Chatham Rise from scientific surveys in 2002, 2011, 2012, and 2013. The data are collected in annual bottom trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA). This research applies clustering via finite mixture models that gives a likelihood-based foundation for the analysis. We use the methods developed by Pledger and Arnold (2014) to cluster species into common groups, conditional on the measured covariates (body size, depth, and water temperature). The project for the first time applies these methods incorporating covariates, and we use simple binary presence/absence data rather than abundances. The models are fitted using the Expectation-Maximization (EM) algorithm. The performance of the models is evaluated by a simulation study. We discuss the advantages and the disadvantages of the EM algorithm. We then introduce a newly developed function clustglm (Pledger et al., 2015) in R, which implements this clustering methodology, and perform our analysis using this function on the real-life presence/absence data. The results are analysed and interpreted from a biological point of view. We present a variety of visualisations of the models to assist in their interpretation. We found that depth is the most important factor to explain the data.</p>

scholarly journals Clustering and Classification in Fisheries

2021 ◽  
Author(s):  
◽  
Yuki Fujita
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Finite Mixture Models ◽  
Real Life ◽  
Point Of View ◽  
Atmospheric Research ◽  
The Em Algorithm ◽  
Clustering And Classification ◽  
First Time ◽  
Trawl Surveys

<p>This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New Zealand waters. In particular we use fish abundance indices on the Chatham Rise from scientific surveys in 2002, 2011, 2012, and 2013. The data are collected in annual bottom trawl surveys carried out by the National Institute of Water and Atmospheric Research (NIWA). This research applies clustering via finite mixture models that gives a likelihood-based foundation for the analysis. We use the methods developed by Pledger and Arnold (2014) to cluster species into common groups, conditional on the measured covariates (body size, depth, and water temperature). The project for the first time applies these methods incorporating covariates, and we use simple binary presence/absence data rather than abundances. The models are fitted using the Expectation-Maximization (EM) algorithm. The performance of the models is evaluated by a simulation study. We discuss the advantages and the disadvantages of the EM algorithm. We then introduce a newly developed function clustglm (Pledger et al., 2015) in R, which implements this clustering methodology, and perform our analysis using this function on the real-life presence/absence data. The results are analysed and interpreted from a biological point of view. We present a variety of visualisations of the models to assist in their interpretation. We found that depth is the most important factor to explain the data.</p>

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 Models and Algorithms for Tracking Target with Coordinated Turn Motion

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Xianghui Yuan ◽  
Feng Lian ◽  
Chongzhao Han
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Multiple Models ◽  
Interacting Multiple Model ◽  
Multiple Model ◽  
Motion Model ◽  
Single Model ◽  
Kinematic Constraint ◽  
The Em Algorithm ◽  
Turn Rate

Tracking target with coordinated turn (CT) motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT) model with known turn rate, augmented coordinated turn (ACT) model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM) framework, the algorithm based on expectation maximization (EM) algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM) algorithm, the EM algorithm shows its effectiveness.

scholarly journals Improved Initialization of the EM Algorithm for Mixture Model Parameter Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 373
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Em Algorithm ◽  
Density Estimation ◽  
Mixture Model ◽  
Expectation Maximization ◽  
State Of The Art ◽  
R Package ◽  
Likelihood Estimator ◽  
Local Optima ◽  
The Em Algorithm ◽  
Initialization Algorithm

A commonly used tool for estimating the parameters of a mixture model is the Expectation–Maximization (EM) algorithm, which is an iterative procedure that can serve as a maximum-likelihood estimator. The EM algorithm has well-documented drawbacks, such as the need for good initial values and the possibility of being trapped in local optima. Nevertheless, because of its appealing properties, EM plays an important role in estimating the parameters of mixture models. To overcome these initialization problems with EM, in this paper, we propose the Rough-Enhanced-Bayes mixture estimation (REBMIX) algorithm as a more effective initialization algorithm. Three different strategies are derived for dealing with the unknown number of components in the mixture model. These strategies are thoroughly tested on artificial datasets, density–estimation datasets and image–segmentation problems and compared with state-of-the-art initialization methods for the EM. Our proposal shows promising results in terms of clustering and density-estimation performance as well as in terms of computational efficiency. All the improvements are implemented in the rebmix R package.

An Estimation of Generalized Bradley-Terry Models Based on the em Algorithm

2011 ◽  
Vol 23 (6) ◽  
pp. 1623-1659 ◽  
Author(s):  
Yu Fujimoto ◽  
Hideitsu Hino ◽  
Noboru Murata
Keyword(s):  
Em Algorithm ◽  
Estimation Method ◽  
Pairwise Comparison ◽  
Information Geometry ◽  
Point Of View ◽  
Ranking Data ◽  
Statistical Representation ◽  
Comparison Results ◽  
The Em Algorithm ◽  
Probability Simplex

The Bradley-Terry model is a statistical representation for one's preference or ranking data by using pairwise comparison results of items. For estimation of the model, several methods based on the sum of weighted Kullback-Leibler divergences have been proposed from various contexts. The purpose of this letter is to interpret an estimation mechanism of the Bradley-Terry model from the viewpoint of flatness, a fundamental notion used in information geometry. Based on this point of view, a new estimation method is proposed on a framework of the em algorithm. The proposed method is different in its objective function from that of conventional methods, especially in treating unobserved comparisons, and it is consistently interpreted in a probability simplex. An estimation method with weight adaptation is also proposed from a viewpoint of the sensitivity. Experimental results show that the proposed method works appropriately, and weight adaptation improves accuracy of the estimate.

scholarly journals Doppler Velocity Estimation of Overlapping Linear-Period-Modulated Ultrasonic Waves Based on an Expectation-Maximization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Natee Thong-un ◽  
Minoru K. Kurosawa
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Pulse Compression ◽  
Moving Objects ◽  
Expectation Maximization Algorithm ◽  
Ultrasonic Waves ◽  
Velocity Estimation ◽  
Doppler Velocity ◽  
The Em Algorithm ◽  
Linear Period

The occurrence of an overlapping signal is a significant problem in performing multiple objects localization. Doppler velocity is sensitive to the echo shape and is also able to be connected to the physical properties of moving objects, especially for a pulse compression ultrasonic signal. The expectation-maximization (EM) algorithm has the ability to achieve signal separation. Thus, applying the EM algorithm to the overlapping pulse compression signals is of interest. This paper describes a proposed method, based on the EM algorithm, of Doppler velocity estimation for overlapping linear-period-modulated (LPM) ultrasonic signals. Simulations are used to validate the proposed method.

Theory and Practice of Expectation Maximization (EM) Algorithm

2011 ◽  
pp. 1966-1973
Author(s):  
Chandan K. Reddy ◽  
Bala Rajaratnam
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Latent Variables ◽  
Likelihood Function ◽  
Optimal Solution ◽  
Local Maximum ◽  
Search Space ◽  
The Em Algorithm ◽  
Log Likelihood ◽  
Estimation Problems

In the field of statistical data mining, the Expectation Maximization (EM) algorithm is one of the most popular methods used for solving parameter estimation problems in the maximum likelihood (ML) framework. Compared to traditional methods such as steepest descent, conjugate gradient, or Newton-Raphson, which are often too complicated to use in solving these problems, EM has become a popular method because it takes advantage of some problem specific properties (Xu et al., 1996). The EM algorithm converges to the local maximum of the log-likelihood function under very general conditions (Demspter et al., 1977; Redner et al., 1984). Efficiently maximizing the likelihood by augmenting it with latent variables and guarantees of convergence are some of the important hallmarks of the EM algorithm. EM based methods have been applied successfully to solve a wide range of problems that arise in fields of pattern recognition, clustering, information retrieval, computer vision, bioinformatics (Reddy et al., 2006; Carson et al., 2002; Nigam et al., 2000), etc. Given an initial set of parameters, the EM algorithm can be implemented to compute parameter estimates that locally maximize the likelihood function of the data. In spite of its strong theoretical foundations, its wide applicability and important usage in solving some real-world problems, the standard EM algorithm suffers from certain fundamental drawbacks when used in practical settings. Some of the main difficulties of using the EM algorithm on a general log-likelihood surface are as follows (Reddy et al., 2008): • EM algorithm for mixture modeling converges to a local maximum of the log-likelihood function very quickly. • There are many other promising local optimal solutions in the close vicinity of the solutions obtained from the methods that provide good initial guesses of the solution. • Model selection criterion usually assumes that the global optimal solution of the log-likelihood function can be obtained. However, achieving this is computationally intractable. • Some regions in the search space do not contain any promising solutions. The promising and nonpromising regions co-exist and it becomes challenging to avoid wasting computational resources to search in non-promising regions. Of all the concerns mentioned above, the fact that most of the local maxima are not distributed uniformly makes it important to develop algorithms that not only help in avoiding some inefficient search over the lowlikelihood regions but also emphasize the importance of exploring promising subspaces more thoroughly (Zhang et al, 2004). This subspace search will also be useful for making the solution less sensitive to the initial set of parameters. In this chapter, we will discuss the theoretical aspects of the EM algorithm and demonstrate its use in obtaining the optimal estimates of the parameters for mixture models. We will also discuss some of the practical concerns of using the EM algorithm and present a few results on the performance of various algorithms that try to address these problems.

scholarly journals Research on Adaptive Optics Image Restoration Algorithm by Improved Expectation Maximization Method

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Lijuan Zhang ◽  
Dongming Li ◽  
Wei Su ◽  
Jinhua Yang ◽  
Yutong Jiang
Keyword(s):  
Em Algorithm ◽  
Image Restoration ◽  
Adaptive Optics ◽  
Expectation Maximization ◽  
Imaging System ◽  
Phase Error ◽  
Estimation Accuracy ◽  
Parameter Estimations ◽  
Power Spectral ◽  
The Em Algorithm

To improve the effect of adaptive optics images’ restoration, we put forward a deconvolution algorithm improved by the EM algorithm which joints multiframe adaptive optics images based on expectation-maximization theory. Firstly, we need to make a mathematical model for the degenerate multiframe adaptive optics images. The function model is deduced for the points that spread with time based on phase error. The AO images are denoised using the image power spectral density and support constraint. Secondly, the EM algorithm is improved by combining the AO imaging system parameters and regularization technique. A cost function for the joint-deconvolution multiframe AO images is given, and the optimization model for their parameter estimations is built. Lastly, the image-restoration experiments on both analog images and the real AO are performed to verify the recovery effect of our algorithm. The experimental results show that comparing with the Wiener-IBD or RL-IBD algorithm, our iterations decrease 14.3% and well improve the estimation accuracy. The model distinguishes the PSF of the AO images and recovers the observed target images clearly.

scholarly journals The expectation maximization algorithm as a powerful tool to solve the stellar membership in open clusters: An application to M67

2006 ◽  
pp. 57-64 ◽  
Author(s):  
A. Uribe ◽  
R. Barrera ◽  
E. Brieva
Keyword(s):  
Em Algorithm ◽  
Expectation Maximization ◽  
Open Cluster ◽  
Expectation Maximization Algorithm ◽  
Open Clusters ◽  
Membership Problem ◽  
Proper Motions ◽  
Bivariate Normal ◽  
Mixture Density ◽  
The Em Algorithm

The EM algorithm is a powerful tool to solve the membership problem in open clusters when a mixture density model overlaping two heteroscedastic bivariate normal components is built to fit the cloud of relative proper motions of the stars in a region of the sky where a cluster is supposed to be. A membership study of 1866 stars located in the region of the very old open cluster M67 is carried out via the Expectation Maximization algorithm using the McLachlan, Peel, Basford and Adams EMMIX software.

scholarly journals Fast Computation of the EM Algorithm for Mixture Models

2021 ◽  
Author(s):  
Masahiro Kuroda
Keyword(s):  
Maximum Likelihood ◽  
Em Algorithm ◽  
Mixture Models ◽  
Computation Time ◽  
Likelihood Estimation ◽  
Heterogeneous Data ◽  
Maximum Likelihood Estimates ◽  
The Em Algorithm ◽  
Clustering And Classification ◽  
Number Of Iterations

Mixture models become increasingly popular due to their modeling flexibility and are applied to the clustering and classification of heterogeneous data. The EM algorithm is largely used for the maximum likelihood estimation of mixture models because the algorithm is stable in convergence and simple in implementation. Despite such advantages, it is pointed out that the EM algorithm is local and has slow convergence as the main drawback. To avoid the local convergence of the EM algorithm, multiple runs from several different initial values are usually used. Then the algorithm may take a large number of iterations and long computation time to find the maximum likelihood estimates. The speedup of computation of the EM algorithm is available for these problems. We give the algorithms to accelerate the convergence of the EM algorithm and apply them to mixture model estimation. Numerical experiments examine the performance of the acceleration algorithms in terms of the number of iterations and computation time.

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