This paper presents a deterministic annealing em (DAem) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional em algorithm. In our approach, a new pos...
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This paper presents a deterministic annealing em (DAem) algorithm for maximum likelihood estimation problems to overcome a local maxima problem associated with the conventional em algorithm. In our approach, a new posterior parameterized by 'temperature' is derived by using the principle of maximum entropy and is used for controlling the annealing process. In the DAem algorithm, the em process is reformulated as the problem of minimizing the thermodynamic free energy by using a statistical mechanics analogy. Since this minimization is deterministically performed at each temperature, the total search is executed far more efficiently than in the simulated annealing. Moreover, the derived DAem algorithm, unlike the conventional em algorithm, can obtain better estimates free of the initial parameter values. We also apply the DAem algorithm to the training of probabilistic neural networks using mixture models to estimate the probability density and demonstrate the performance of the DAem algorithm. (C) 1998 Elsevier Science Ltd. All rights reserved.
This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (em) algorithm to estimate the basis function parameters. The resulting...
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This paper proposes to incorporate full covariance matrices into the radial basis function (RBF) networks and to use the expectation-maximization (em) algorithm to estimate the basis function parameters. The resulting networks, referred to as elliptical basis function (EBF) networks, are evaluated through a series of text-independent speaker verification experiments involving 258 speakers from a phonetically balanced, continuous speech corpus (TIMIT), We propose a verification procedure using RBF and EBF networks as speaker models and show that the networks are readily applicable to verifying speakers using LP-derived cepstral coefficients as features. Experimental results show that small EBF networks with basis function parameters estimated by the em algorithm outperform the large RBF networks trained in the conventional approach, The results also show that the equal error rate achieved by the EBF networks is about two-third of that achieved by the vetor quantization (VQ)-based speaker models.
This paper shows how multiple shape hypotheses can be used to recognise complex line patterns using the expectation-maximisation algorithm. The idea underpinning this work is to construct a mixture distribution for an...
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This paper shows how multiple shape hypotheses can be used to recognise complex line patterns using the expectation-maximisation algorithm. The idea underpinning this work is to construct a mixture distribution for an observed configuration of line segments over a space of hypothesised shape models. According to the em framework each model is represented by a set of maximum likelihood registration parameters together with a set of matching probabilities. These two pieces of information are iteratively updated so as to maximise the expected data likelihood over the space of model-data associations. This architecture can be viewed as providing simultaneous shape registration and hypothesis verification. We illustrate the effectiveness of the recognition strategy by studying the registration of noisy radar data against a database of alternative cartographic maps for different locations. (C) 1997 Elsevier Science B.V.
Some species of birds are known to use memory to retrieve previously stored food. A series of experiments on one of those species, coal tits, investigated various aspects of such memory. For one particular experiment,...
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Some species of birds are known to use memory to retrieve previously stored food. A series of experiments on one of those species, coal tits, investigated various aspects of such memory. For one particular experiment, a number of statistical models describing the memory were fitted. However, some of the data were unavoidably incomplete. The expectation maximization (em) algorithm provides a means of incorporating the incomplete data into the fitting procedure.
The expectation maximization (em) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals, Alternative regularized least squares estimation techniques for the ...
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The expectation maximization (em) algorithm is presented for the case of estimating direction of arrivals of unknown deterministic wideband signals, Alternative regularized least squares estimation techniques for the required signal estimation and a tree structure for the data mapping in the Ehl algorithm are proposed. Extensive simulation results are presented for comparison of the proposed algorithms with the conventional em approach and the current high-resolution methods of wideband direction finding.
Clustering is a simple, effective way to derive useful representations of data, such as images and videos. Clustering explains the input as one of several prototypes, plus noise. In situations where each input has bee...
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Clustering is a simple, effective way to derive useful representations of data, such as images and videos. Clustering explains the input as one of several prototypes, plus noise. In situations where each input has been randomly transformed (e.g., by translation, rotation, and shearing in images and videos), clustering techniques tend to extract cluster centers that account for variations in the input due to transformations, instead of more interesting and potentially useful structure. For example, if images from a video sequence of a person walking across a cluttered background are clustered, it would be more useful for the different clusters to represent different poses and expressions, instead of different positions of the person and different configurations of the background clutter. We describe a way to add transformation invariance to mixture models, by approximating the nonlinear transformation manifold by a discrete set of points. We show how the expectation maximization algorithm can be used to jointly learn clusters, while at the same time inferring the transformation associated with each input. We compare this technique with other methods for filtering noisy images obtained from a scanning electron microscope, clustering images from videos of faces into different categories of identification and pose and removing foreground obstructions from video. We also demonstrate that the new technique is quite insensitive to initial conditions and works better than standard techniques, even when the standard techniques are provided with extra data.
The standard em (estimate, maximize) algorithm exhibits very slow convergence. In the special test case where the underlying positive linear system has a unique solution, we describe two iterations, based on adaptivel...
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The standard em (estimate, maximize) algorithm exhibits very slow convergence. In the special test case where the underlying positive linear system has a unique solution, we describe two iterations, based on adaptively choosing the smoothing in the emS (estimate, maximize, smooth) algorithm, which accelerate the convergence of the em algorithm. The resulting algorithms, called adaptive emS (A-emS) algorithms, thus overcome the inaccuracy of emS while retaining its more rapid convergence and reduced overall computational cost.
Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The...
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Plant disease leaf image segmentation plays an important role in the plant disease detection through leaf symptoms. A novel segmentation method of plant disease leaf image is proposed based on a hybrid clustering. The whole color leaf image is firstly divided into a number of compact and nearly uniform superpixels by superpixel clustering, which can provide useful clustering cues to guide image segmentation to accelerate the convergence speed of the expectation maximization (em) algorithm, and then, the lesion pixels are quickly and accurately segmented from each superpixel by em algorithm. The experimental results and the comparison results with similar approaches demonstrate that the proposed method is effective and has high practical value for plant disease detection.
In this article, using longitudinal data, we develop the theory of credibility by copula model. The convex combination of copulas is used to describe the dependencies among claims. Finally, for comparing with the resu...
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In this article, using longitudinal data, we develop the theory of credibility by copula model. The convex combination of copulas is used to describe the dependencies among claims. Finally, for comparing with the results of a single copula, using em algorithm, some simulations of Massachusetts automobile claims are presented.
The analysis of biological networks is an important task in life sciences. Most of biological interactions can be modeled using graphical networks where arcs represent probabilistic relationships between nodes or vari...
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The analysis of biological networks is an important task in life sciences. Most of biological interactions can be modeled using graphical networks where arcs represent probabilistic relationships between nodes or variables. Such models help scientists to analyze their complex data sets, test candidate interaction networks and understand the studied relationships. These studies face two major problems: the selection of most probable interaction topologies and the clustering of the associated peculiar data. In this paper, we model biological interactions with a mixture of multivariate Gaussian distributions. We, then, introduce a new algorithm for the parameters estimation and data clustering. This algorithm, called Graphical Expectation Maximization (Gem), extends the em algorithm by taking into account several decomposable graph structures and using an original initialization technique. Applying this algorithm, we propose a model selection procedure based on the Bayesian Information Criterion. The accuracy of the proposed method is demonstrated on the grounds of a simulation study of a signal transduction network of the epidermal growth factor (EGFR) protein. Moreover, we apply the proposed model selection procedure to choose the most appropriate interaction graphs for microbial community in infant gut using a real data set. (C) 2019 Elsevier B.V. All rights reserved.
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