A finite mixture model has been used to fit the data from heterogeneous populations to many applications. An expectationmaximization (EM) algorithm is the most popular method to estimate parameters in a finite mixtur...
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A finite mixture model has been used to fit the data from heterogeneous populations to many applications. An expectationmaximization (EM) algorithm is the most popular method to estimate parameters in a finite mixture model. A Bayesian approach is another method for fitting a mixture model. However, the EM algorithm often converges to the local maximum regions, and it is sensitive to the choice of starting points. In the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sometimes converges to the local mode and is difficult to move to another mode. Hence, in this paper we propose a new method to improve the limitation of EM algorithm so that the EM can estimate the parameters at the global maximum region and to develop a more effective Bayesian approach so that the MCMC chain moves from one mode to another more easily in the mixture model. Our approach is developed by using both simulated annealing (SA) and adaptive rejection metropolis sampling (ARMS). Although SA is a well-known approach for detecting distinct modes, the limitation of SA is the difficulty in choosing sequences of proper proposal distributions for a target distribution. Since ARMS uses a piecewise linear envelope function for a proposal distribution, we incorporate ARMS into an SA approach so that we can start a more proper proposal distribution and detect separate modes. As a result, we can detect the maximum region and estimate parameters for this global region. We refer to this approach as ARMS annealing. By putting together ARMS annealing with the EM algorithm and with the Bayesian approach, respectively, we have proposed two approaches: an EM-ARMS annealing algorithm and a Bayesian-ARMS annealing approach. We compare our two approaches with traditional EM algorithm alone and Bayesian approach alone using simulation, showing that our two approaches are comparable to each other but perform better than EM algorithm alone and Bayesian approach alone. Our two approaches detect the global maxi
We first examine the techniques,development,and application future of the current recommender systems in the film *** recommendation techniques in current applications and the K-nearest neighbor（***） algorithm,in pa...
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We first examine the techniques,development,and application future of the current recommender systems in the film *** recommendation techniques in current applications and the K-nearest neighbor（***） algorithm,in particular,is then introduced in *** is followed by an introduction to the expectationmaximization（***） algorithm based on the Bayesian classifier,which has been applied to the classification and similarity calculations of ***,the movieeviews data in the NLTK（Natural Language Toolkit）library is used to facilitate *** evaluate the classification accuracy of the KNN algorithm and the EM algorithm based on the Bayesian *** experimental results demonstrate that,the classification accuracy of the EM algorithm for films is higher than that of the KNN algorithm and it is feasible and useful to apply the EM algorithm to films classification.
Magnetic Resonance Image segmentation is a fundamental task in a wide variety of computed-based medical applications that support therapy, diagnostic and medical applications. In this work, spatial information is incl...
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ISBN:
(纸本)9783642166860
Magnetic Resonance Image segmentation is a fundamental task in a wide variety of computed-based medical applications that support therapy, diagnostic and medical applications. In this work, spatial information is included for estimating paramaters of a finite mixture model, with gaussian distribution assumption, using a modified version of the well-know expectation maximization algorithm proposed in [3]. Our approach is based on aggregating a transition step between E-step and M-step, that includes the information of spatial dependences between neighboring pixels. Our proposal is compared with other approaches proposed in the image segmentation literature using the size and shape test, obtaining accurate and robust results in the presence of noise.
DNA microarrays provide a simple tool to identify and quantify the gene expression for tens of thousands of genes simultaneously. Image processing is an important step in microarrays experiments. This paper presents a...
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ISBN:
(纸本)9781424447138
DNA microarrays provide a simple tool to identify and quantify the gene expression for tens of thousands of genes simultaneously. Image processing is an important step in microarrays experiments. This paper presents a novel technique for removing gene's noises based on the offset vector field and segmenting genes using the expectation maximization algorithm. Simulations show that the new technique for microarray images filtering and segmentation has better performance than most of the common ways. The results of experiments are computationally attractive, have excellent performance and can preserve spots' data while efficiently suppress noises.
The assumptions on the hidden Markov model will limit the output of a model to be a piecewise stationary random sequence that may not be a good fit for real processes. In this paper we propose a piecewise polynomial h...
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ISBN:
(纸本)9781509043149
The assumptions on the hidden Markov model will limit the output of a model to be a piecewise stationary random sequence that may not be a good fit for real processes. In this paper we propose a piecewise polynomial high-order hidden Markov model so that the output of a model can be more versatile. We derived formulas for the calculation of the probability that a given sequence is produced by a model. We also derived the posterior probability of the states and the state transitions and used them in an expectation maximization algorithm to update the model parameters. Experiments on speech recognition of Mandarin digits were conducted to investigate the effectiveness of the proposed model. Experimental results showed that the proposed model can reduce the recognition error rate compared to a baseline hidden Markov model.
A key issue in mission planning for aerial reconnaissance is to use the sensor ressources in an appropriate way. The sensor mission planning requires knowledge, e.g., about the optimal sensor type (IR/EO) or the neces...
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ISBN:
(纸本)9781510600928
A key issue in mission planning for aerial reconnaissance is to use the sensor ressources in an appropriate way. The sensor mission planning requires knowledge, e.g., about the optimal sensor type (IR/EO) or the necessary flying altitude for a specific task. There are various types of task that can be part of the mission, e.g., to detect a vehicle or investigate a bridge. The goal of this work is to examine knowledge-based approaches like ontologies and the use of them to automatically derive all needed parameters for an optimal sensor mission planning based on the task. The task-oriented mission planning is processed on the tactical level. Based on the task the aerial image analyst defines the specific evaluation conditions. Various parameters are part of the task-oriented mission planning. For example there are the scene, the flight mode, the sensor, the system and the image analysis parameters. We introduce an idea to represent the sensor mission planning task at a relatively high-level knowledge-based approach. We want to create a representation of a useful sensor mission planning where all aspects and its components are considered, what these parts do, how they relate to each other and define the rules and constraints within. The main framework is based on the definitions of target categories and purpose codes (STANAG 3596) and NIIRS level.
Classical wireless communication technologies are threatened with so many challenges for meeting the desires of ubiquity and mobility for the cellular systems. Hostile wireless channels features and restricted frequen...
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ISBN:
(纸本)9781509002108
Classical wireless communication technologies are threatened with so many challenges for meeting the desires of ubiquity and mobility for the cellular systems. Hostile wireless channels features and restricted frequency bandwidths are obstacles in future generation systems. In order to deal with these limitations, different advanced signal processing approaches, such as expectation-maximization (EM) algorithm, SAGE algorithm, Baum-Welch algorithm, Kalman filters and their extensions etc. were proposed. In this paper, estimation of unknown channel parameter and detection of data at receiver end has been performed. MIMO Rayleigh and Rician channels are taken for wireless communication. To find the initial point for EM algorithm FCM clustering algorithm is used. In this work, algorithm is implemented using MATLAB R2012a. The performance matrices of the algorithm are bit error rate (BER) and mean square error (MSE) at different values of signal to noise ratio.
In this paper, we consider Generalized Gaussian (GG) distribution to model the additive noise source in underwater acoustic (UWA) communication. Since communication in oceanic medium is dominated by both prevailing an...
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ISBN:
(纸本)9781509017461
In this paper, we consider Generalized Gaussian (GG) distribution to model the additive noise source in underwater acoustic (UWA) communication. Since communication in oceanic medium is dominated by both prevailing and spontaneous noise sources, we model the resultant noise distribution as mixture of GG distribution. Owing to the complexity in optimal detector design with GG noise model, we apply expectationmaximization (EM) algorithm to decompose the resultant channel distribution in terms of weighted sum of Gaussian density functions. By having multiple antennas at the receiver, we also exploit spatial diversity to improve error performance at the receiver. In this context, we compute decision boundary for detecting the binary phase shift keying (BPSK) modulated signal. In addition, we also discuss variation in decision boundary under various signal to noise ratio (SNR) levels observed at receiver front end. Finally, we compare the detector performance under new decision boundary with traditional detectors and validate the approach by showing improvement in symbol error rate performance.
This paper considers the problem of networks reconstruction from heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well known that parameter estimation in this context is challenging due to lar...
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This paper considers the problem of networks reconstruction from heterogeneous data using a Gaussian Graphical Mixture Model (GGMM). It is well known that parameter estimation in this context is challenging due to large numbers of variables coupled with the degenerate nature of the likelihood. We propose as a solution a penalized maximum likelihood technique by imposing an l1 penalty on the precision matrix. Our approach shrinks the parameters thereby resulting in better identifiability and variable selection. Cet article considère le probléme de la reconstruction de réseaux à partir de données hétérogènes en utilisant le modèle graphique gaussien mélangé (GGMM en Anglais). Il est connu que l'estimation paramétrique, dans ce contexte, n'est pas aisé à cause du grand nombre de variable et de la nature dégénérée de la vraisemblance. Nous proposons comme une solution une méthode de pénalisation du maximum de vraisemblance en imposant une pénalité de type l1 sur la précision de la matrice. Notre méthode réduit les paramétres et ainsi aboutit à une meilleure identification et à un meilleur choix des variables.
We consider a system defined as a collection of two types of components. The number of failures of each component is described as a stochastic process, with one of the processes depending on the other. None of the pro...
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We consider a system defined as a collection of two types of components. The number of failures of each component is described as a stochastic process, with one of the processes depending on the other. None of the processes is observed directly. The only available information is the number of type 1 components at risk in the system. Because of this missing data situation, different algorithms relying on an expectationmaximization (EM) strategy are proposed to obtain the MLE of the intensity parameters for both processes so we can assess the reliability of type 1 and type 2 components. To overcome the computational limits of EM, a Monte Carlo EM (MCEM) algorithm using a Metropolis procedure is presented. Stochastic EM (SEM) algorithms including a Bayesian approach are also described. The methods are applied to simulated data to demonstrate their efficiency.
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