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IEEE International Symposium on Circuits and Systems

作者： Pumrin, S Dailey, DJ Chulalongkorn Univ Dept Elect Engn Bangkok 10330 Thailand

ISBN: (纸本)0780377613

In this paper, we present a statistical method to extract images. of passenger cars from highway traffic scenes. The expectation-maximization (EM) algorithm is used to classify the vehicles in the images as' either being passenger cars or some other bigger vehicles, cars versus non-cars. The vehicle classification algorithm uses training sets of 100-frame video sequences. The car group is comprised of passenger cars and light trucks. The non-car group is comprised of heavy single trucks as well as 3-axle and more combination trucks. We use the properties of their dimensional,distribution and the probability of their appearance to identify the vehicle group. We present a validation of our algorithm using real-world traffic scenes.

关键词： image classification intelligent transportation systems expectation-maximization algorithm

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Variational learning for Gaussian mixture models

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2006年第4期36卷 849-862页

作者： Nasios, Nikolaos Bors, Adrian G. Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

This paper proposes a joint maximum likelihood and Bayesian methodology for estimating Gaussian mixture models. In Bayesian inference, the distributions of parameters are modeled, characterized by hyperparameters. In the case of Gaussian mixtures, the distributions of parameters are considered as Gaussian for the mean, Wishart for the covariance, and Dirichlet for the mixing probability. The learning task consists of estimating the hyperparameters characterizing these distributions. The integration in the parameter space is decoupled using an unsupervised variational methodology entitled variational expectation-maximization (VEM). This paper introduces a hyperparameter initialization procedure for the training algorithm. In the first stage, distributions of parameters resulting from successive runs of the expectation-maximization algorithm are formed. Afterward, maximum-likelihood estimators are applied to find appropriate initial values for the hyperparameters. The proposed initialization provides faster convergence, more accurate hyperparameter estimates, and better generalization for the VEM training algorithm. The proposed methodology is applied in blind signal detection and in color image segmentation.

关键词： Bayesian inference expectation-maximization algorithm Gaussian mixtures maximum log-likelihood estimation variational training

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A hybrid EM approach to spatial clustering

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2006年第5期50卷 1188-1205页

作者： Hu, TM Sung, SY Natl Univ Singapore Dept Comp Sci Singapore 117543 Singapore

Spatial clustering requires consideration of spatial information and this makes expectation-maximization (EM) algorithm that maximizes likelihood alone inappropriate. Although neighborhood EM (NEM) algorithm incorporates a spatial penalty term, it needs much more iterations for E-step. To incorporate spatial information while avoiding much additional computation, we propose a hybrid EM (HEM) approach that combines EM and NEM. Early training is performed via a selective hard EM till the penalized likelihood criterion begins to decrease. Then training is turned to NEM, which runs only one iteration of E-step and plays a role of finer tuning. Thus spatial information is incorporated throughout HEM and the computational complexity is also comparable to EM. Empirical results show that a few more passes are needed in HEM to converge after switching to NEM and the final clustering quality is close to or slightly better than standard NEM. (C) 2005 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithm spatial clustering Gaussian mixture spatial penalty term

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Segmentation of trabeculated structures using an anisotropic Markov random field: Application to the study of the optic nerve head in glaucoma

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2006年第3期25卷 245-255页

作者： Grau, V Downs, JC Burgoyne, CF Louisiana State Univ Hlth Sci Ctr LSU Eye Ctr New Orleans LA 70112 USA Tulane Univ Dept Biomed Engn New Orleans LA 70118 USA

The study of the architecture of the optic nerve head (ONH) may provide valuable information about the development and progression of glaucoma. To this end, we have generated three-dimensional datasets from monkey eyes under controlled intraocular pressure (IOP). Segmentation of the connective tissues in this area is crucial to obtain an accurate measurement of geometrical parameters and to build mechanical models. However, this segmentation is made difficult by the complicated geometry and the artifacts introduced in the dataset building process. We present a novel segmentation algorithm, based on expectation-maximization, which incorporates an anisotropic Markov random field (MRF) to introduce prior knowledge about the geometry of the structure. The structure tensor is used to characterize the predominant structure direction and the spatial coherence at each point. The algorithm, which has been validated on an artificial validation dataset that mimics our ONH datasets, shows significant improvement over an isotropic MRF. Results on the real datasets demonstrate the ability of the new algorithm to obtain accurate, spatially consistent segmentations of this structure.

关键词： expectation-maximization algorithm hidden Markov models image segmentation optic nerve head

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Finding cancer subtypes in microarray data using random projections

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NEUROCOMPUTING 2006年第16-18期69卷 2258-2267页

作者： Ghosh, Debashis Univ Michigan Sch Publ Hlth Dept Biostat Ann Arbor MI 48109 USA

One of the benefits of profiling cancer samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Such subgroups have typically been found in microarray data using hierarchical clustering. A major problem in the interpretation of the output is determining the number of clusters. We approach the problem of determining disease subtypes using mixture models. A novel estimation procedure for the parameters in the mixture model is developed based on a combination of random projections and the expectation-maximization algorithm. Because the approach is probabilistic, our method provides a measure for the number of true clusters in a given dataset. We illustrate our proposal with applications to both simulated and real microarray data. (c) 2005 Elsevier B.V. All rights reserved.

关键词： expectation-maximization algorithm gene expression data high-dimensional data mixture models

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Network motif identification in stochastic networks

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2006年第25期103卷 9404-9409页

作者： Jiang, Rui Tu, Zhidong Chen, Ting Sun, Fengzhu Univ So Calif Mol & Computat Biol Program Los Angeles CA 90089 USA

Network motifs have been identified in a wide range of networks across many scientific disciplines and are suggested to be the basic building blocks of most complex networks. Nonetheless, many networks come with intrinsic and/or experimental uncertainties and should be treated as stochastic networks. The building blocks in these networks thus may also have stochastic properties. In this article, we study stochastic network motifs derived from families of mutually similar but not necessarily identical patterns of interconnections. We establish a finite mixture model for stochastic networks and develop an expectation-maximization algorithm for identifying stochastic network motifs. We apply this approach to the transcriptional regulatory networks of Escherichia coli and Saccharomyces cerevisiae, as well as the protein-protein interaction networks of seven species, and identify several stochastic network motifs that are consistent with current biological knowledge.

关键词： expectation-maximization algorithm mixture model transcriptional regulatory network protein-protein interaction network

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Mixture model analysis of DNA microarray images

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2005年第7期24卷 901-909页

作者： Blekas, K Galatsanos, NP Likas, A Lagaris, IE Univ Ioannina Dept Comp Sci GR-45110 Ioannina Greece

In this paper, we propose a new methodology for analysis of microarray images. First, a new gridding algorithm is proposed for determining the individual spots and their borders. Then, a Gaussian mixture model (GMM) approach is presented for the analysis of the individual spot images. The main advantages of the proposed methodology are modeling flexibility and adaptability to the data, which are well-known strengths of GMM. The maximum likelihood and maximum a posteriori approaches are used to estimate the GMM parameters via the expectation maximization algorithm. The proposed approach has the ability to detect and compensate for artifacts that might occur in microarray images. This is accomplished by a model-based criterion that selects the number of the mixture components. We present numerical experiments with artificial and real data where we compare the proposed approach with previous ones and existing software tools for microarray image analysis and demonstrate its advantages.

关键词： cross-validated likelihood DNA microarray image analysis expectation-maximization algorithm Gaussian mixture models Markov random fields maximum a posteriori maximum likelihood microarray gridding

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A mixture of experts network structure for modelling Doppler ultrasound blood flow signals

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COMPUTERS IN BIOLOGY AND MEDICINE 2005年第7期35卷 565-582页

作者： Güler, I Übeyli, ED Gazi Univ Fac Tech Educ Dept Elect & Comp Engn TR-06500 Ankara Turkey

Mixture of experts (ME) is a modular neural network architecture for supervised learning. This paper illustrates the use of ME network structure to guide modelling Doppler ultrasound blood flow signals. expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The ophthalmic and internal carotid arterial Doppler signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structures were implemented for diagnosis of ophthalmic and internal carotid arterial disorders using the statistical features as inputs. To improve diagnostic accuracy, the outputs of expert networks were combined by a gating network simultaneously trained in order to stochastically select the expert that is performing the best at solving the problem. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. (c) 2004 Elsevier Ltd. All rights reserved.

关键词： mixture of experts expectation-maximization algorithm diagnostic accuracy discrete wavelet transform Doppler signal ophthalmic artery internal carotid artery

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Unsupervised restoration of hidden nonstationary Markov chains using evidential priors

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2005年第8期53卷 3091-3098页

作者： Lanchantin, P Pieczynski, W Inst Natl Telecommun GET INT Dept CITI CNRS UMR 5157 F-91011 Evry France

This paper addresses the problem of unsupervised Bayesian hidden Markov chain restoration. When the hidden chain is stationary, the classical "Hidden Markov Chain" (HMC) model is quite efficient, and associated unsupervised Bayesian restoration methods using the "expectation-maximization" (EM) algorithm work well. When the hidden chain is non stationary, on the other hand, the unsupervised restoration results using the HMC model can be poor, due to a bad match between the real and estimated models. The novelty of this paper is to offer a more appropriate model for hidden nonstationary Markov chains, via the theory of evidence. Using recent results relating to Triplet Markov Chains (TMCs), we show, via simulations, that the classical restoration results can be improved by the use of the theory of evidence and Dempster-Shafer fusion. The latter improvement is performed in an entirely unsupervised way using an original parameter estimation method. Some application examples to unsupervised image segmentation are also provided.

关键词： Bayesian restoration Dempster-Shafer fusion expectation-maximization algorithm Hidden Markov chains nonstationary Markov chain restoration parameter estimation theory of evidence

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A modified mixture of experts network structure for ECG beats classification with diverse features

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2005年第7期18卷 845-856页

作者： Güler, I Übeyli, ED Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey TOBB Ekon & Teknol Univ Fac Engn Dept Elect & Elect Engn TR-06530 Ankara Turkey

This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of electrocardiogram (ECG) beats with diverse features. The MME is a modular neural network architecture for supervised learning. expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. The wavelet coefficients and Lyapunov exponents of the ECG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the ECG signals, were then input into the MME network structure for training and testing purposes. We explored the ability of designed and trained MME network structure, combined with wavelet preprocessing (computing wavelet coefficients) and nonlinear dynamics tools (computing Lyapunov exponents), to discriminate five types of ECG beats (normal beat, congestive heart failure beat, ventricular tachyarrhythmia beat, atrial fibrillation beat, partial epilepsy beat) obtained from the Physiobank database. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying long-term ECG signals for early detection of heart diseases/abnormalities. (c) 2005 Elsevier Ltd. All rights reserved.

关键词： modified mixture of experts expectation-maximization algorithm wavelet coefficients Lyapunov exponents ECG beats classification diverse features

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