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Multiphase learning for an interval-based hybrid dynamical system

IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES 2005年第11期E88A卷 3022-3035页

作者： Kawashima, H Matsuyama, T Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

This paper addresses the parameter estimation problem of an interval-based hybrid dynamical system (interval system). The interval system has a two-layer architecture that comprises a finite state automaton and multiple linear dynamical systems. The automaton controls the activation timing of the dynamical systems based on a stochastic transition model between intervals. Thus, the interval system can generate and analyze complex multivariate sequences that consist of temporal regimes of dynamic primitives. Although the interval system is a powerful model to represent human behaviors such as gestures and facial expressions, the learning process has a paradoxical nature: temporal segmentation of primitives and identification of constituent dynamical systems need to be solved simultaneously. To overcome this problem, we propose a multiphase parameter estimation method that consists of a bottom-up clustering phase of linear dynamical systems and a refinement phase of all the system parameters. Experimental results show the method can organize hidden dynamical systems behind the training data and refine the system parameters successfully.

关键词： hybrid dynamical system interval transition system identification clustering of dynamical systems expectation-maximization algorithm

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Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation

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AMERICAN JOURNAL OF HUMAN GENETICS 2005年第3期76卷 449-462页

作者： Stephens, M Scheet, P Univ Washington Dept Stat Seattle WA 98195 USA

Although many algorithms exist for estimating haplotypes from genotype data, none of them take full account of both the decay of linkage disequilibrium (LD) with distance and the order and spacing of genotyped markers. Here, we describe an algorithm that does take these factors into account, using a flexible model for the decay of LD with distance that can handle both "blocklike" and "nonblocklike" patterns of LD. We compare the accuracy of this approach with a range of other available algorithms in three ways: for reconstruction of randomly paired, molecularly determined male X chromosome haplotypes;for reconstruction of haplotypes obtained from trios in an autosomal region;and for estimation of missing genotypes in 50 autosomal genes that have been completely resequenced in 24 African Americans and 23 individuals of European descent. For the autosomal data sets, our new approach clearly outperforms the best available methods, whereas its accuracy in inferring the X chromosome haplotypes is only slightly superior. For estimation of missing genotypes, our method performed slightly better when the two subsamples were combined than when they were analyzed separately, which illustrates its robustness to population stratification. Our method is implemented in the software package PHASE (v2.1.1), available from the Stephens Lab Web site.

关键词： expectation-maximization algorithm GENOTYPE DATA POPULATION-GENETICS RECOMBINATION RATES HUMAN GENOME RECONSTRUCTION SAMPLES

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Pairwise likelihood inference in spatial generalized linear mixed models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2005年第4期49卷 1173-1191页

作者： Varin, C Host, G Skare, O Univ Padua Dept Stat I-35121 Padua Italy Res Council Norway Oslo Norway Univ Oslo Inst Biol Oslo Norway

Spatial generalized linear mixed models are flexible models for a variety of applications, where spatially dependent and non-Gaussian random variables are observed. The focus is inference in spatial generalized linear mixed models for large data sets. Maximum likelihood or Bayesian Markov chain Monte Carlo approaches may in such cases be computationally very slow or even prohibitive. Alternatively, one may consider a composite likelihood, which is the product of likelihoods of subsets of data. In particular, a composite likelihood based on pairs of observations is adopted. In order to maximize the pairwise likelihood, a new expectation-maximization-type algorithm which uses numerical quadrature is introduced. The method is illustrated on simulated data and on data from air pollution effects for fish populations in Norwegian lakes. A comparison with alternative methods is given. The proposed algorithm is found to give reasonable parameter estimates and to be computationally efficient. (c) 2004 Elsevier B.V. All rights reserved.

关键词： composite likelihood expectation-maximization algorithm Gauss-Hermite quadrature model-based geostatistics pairwise likelihood

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HAPLORE: a program for haplotype reconstruction in general pedigrees without recombination

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BIOINFORMATICS 2005年第1期21卷 90-103页

作者： Zhang, K Sun, FZ Zhao, HY Yale Univ Sch Med Dept Epidemiol & Publ Hlth New Haven CT 06520 USA Univ Alabama Birmingham Dept Biostat Sect Stat Genet Birmingham AL 35294 USA Univ So Calif Dept Biol Sci Mol & Computat Biol Program Los Angeles CA 90089 USA

Motivation: Haplotype reconstruction is an essential step in genetic linkage and association studies. Although many methods have been developed to estimate haplotype frequencies and reconstruct haplotypes for a sample of unrelated individuals, haplotype reconstruction in large pedigrees with a large number of genetic markers remains a challenging problem. Methods: We have developed an efficient computer program, HAPLORE (HAPLOtype REconstruction), to identify all haplotype sets that are compatible with the observed genotypes in a pedigree for tightly linked genetic markers. HAPLORE consists of three steps that can serve different needs in applications. In the first step, a set of logic rules is used to reduce the number of compatible haplotypes of each individual in the pedigree as much as possible. After this step, the haplotypes of all individuals in the pedigree can be completely or partially determined. These logic rules are applicable to completely linked markers and they can be used to impute missing data and check genotyping errors. In the second step, a haplotype-elimination algorithm similar to the genotype-elimination algorithms used in linkage analysis is applied to delete incompatible haplotypes derived from the first step. All superfluous haplotypes of the pedigree members will be excluded after this step. In the third step, the expectation-maximization (EM) algorithm combined with the partition and ligation technique is used to estimate haplotype frequencies based on the inferred haplotype configurations through the first two steps. Only compatible haplotype configurations with haplotypes having frequencies greater than a threshold are retained. Results: We test the effectiveness and the efficiency of HAPLORE using both simulated and real datasets. Our results show that, the rule-based algorithm is very efficient for completely genotyped pedigree. In this case, almost all of the families have one unique haplotype configuration. In the presence of missi

关键词： expectation-maximization algorithm AUTOMATIC GENOTYPE ELIMINATION LINKAGEDISEQUILIBRIUM FREQUENCY ESTIMATION DIPLOID POPULATIONS PROGENY GENOTYPES INFERENCE LIKELIHOOD SAMPLES CONSTRUCTION

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Reconstruction of cloud-contaminated multitemporal optical images with a contextual prediction method

Reconstruction of cloud-contaminated multitemporal optical i...

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25th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2005)

作者： Melgani, F Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy

ISBN: (纸本)0780390504

The aim of this work, is to propose a new single-channel method for the reconstruction of areas obscured by clouds in a sequence of temporal optical images. Given a cloud-contaminated image of the sequence, each area of missina measurements is reconstructed by means of an ensemble of contextual linear predictors that reproduce the local spectro-temporal relationships between the considered image and an opportunely selected subset of the remaining temporal images. Each predictor of the ensemble is trained in an unsupervised way, over a local multitemporal region that is spectrally homogeneous in each temporal image of the selected partial sequence. In order to obtain such regions, each temporal image is locally classified by, means of an unsupervised classifier based on the expectation-maximization (EM) algorithm. To illustrate the performance of the proposed method, an experimental analysis on a sequence of three temporal images acquired by the Landsat-7 ETM+ sensor is reported.

关键词： cloud removal image reconstruction spatio-temporal context linear prediction expectation-maximization algorithm

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A mixture of experts network structure for EEG signals classification

A mixture of experts network structure for EEG signals class...

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27th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Guler, Inan Ubeyli, Elif Derya Guler, Nihal Fatma Gazi Univ Fac Tech Educ Dept Elect & Comp Educ TR-06500 Ankara Turkey

ISBN: (纸本)0780387406

This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) 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 EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The ME network structure was implemented for classification of the EEG signals using the statistical features as inputs. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 93.17% by the ME network structure. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models.

关键词： mixture of experts expectation-maximization algorithm discrete wavelet transform EEG signals classification

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A mixture of experts network structure for breast cancer diagnosis

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Journal of Medical Systems 2005年第5期29卷 569-579页

作者： Übeyli, Elif Derya Department of Electrical and Electronics Engineering Faculty of Engineering TOBB Ekonomi Ve Teknoloji Üniversitesi Çankaya Ankara Sogutozu 06560 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 diagnosing of breast cancer. 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. Diagnosis tasks are among the most interesting activities in which to implement intelligent systems. Specifically, diagnosis is an attempt to accurately forecast the outcome of a specific situation, using as input information obtained from a concrete set of variables that potentially describe the situation. The ME network structure was implemented for breast cancer diagnosis using the attributes of each record in the Wisconsin breast cancer database. 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. For the Wisconsin breast cancer diagnosis problem, the obtained total classification accuracy by the ME network structure was 98.85%. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 Springer Science+Business Media, Inc.

关键词： Breast cancer diagnosis Diagnostic accuracy expectation-maximization algorithm Mixture of experts

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Multichannel audio resynthesis based on a Generalized Gaussian mixture model and cepstral smoothing

Multichannel audio resynthesis based on a Generalized Gaussi...

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Workshop on Applications of Signal Processing to Audio and Acoustics

作者： Cantzos, D Mouchtaris, A Kyriakakis, C Univ So Calif IMSC Los Angeles CA 90089 USA

ISBN: (纸本)0780391543

Multichannel audio is all emerging technology with continuously increasing applications. Audio reproduction through multiple channels has the advantage of recreating file acoustic Scene with unprecedented fidelity and of immersing the listener in an acoustic environment that is virtually indistinguishable from reality. However. one of the greatest challenges of this scheme is its high transmission requirements especially since accurate rendering through as many possible channels is the main purpose. This paper follows previous techniques Oil spectral conversion and it recently introduced concept called audio resynthesis. In audio resynthesis. a reference channel is transmitted and then used to recreate the remaining channels at the receiver. An alternative approach to audio resynthesis is presented based oil the Generalized Gaussian Mixture model. This model incorporates most of the standard Mixtures (Laplace. Gaussian etc) but this flexibility comes with high structural complexity due to file increased number of model parameters. A scheme is presented here that bypasses this issue and avoids the use of the expectation-maximization (EM) algorithm. A smoothing technique is also introduced which optimizes the performance during the spectral conversion stage and significantly improves file resynthesis results.

关键词： Gaussian processes audio signal processing cepstral analysis expectation-maximisation algorithm sound reproduction cepstral smoothing expectation-maximization algorithm generalized Gaussian mixture model multichannel audio resynthesis reference channel s

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Process monitoring using a Gaussian mixture model via principal component analysis and discriminant analysis

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COMPUTERS & CHEMICAL ENGINEERING 2004年第8期28卷 1377-1387页

作者： Choi, SW Park, JH Lee, IB Pohang Univ Sci & Technol Dept Chem Engn Pohang 790784 South Korea Pohang Univ Sci & Technol Sch Environm Sci & Engn Pohang 790784 South Korea Pohang Univ Sci & Technol P&I Consulting Co Ltd Pohang 790784 South Korea

Conventional process monitoring based on principal component analysis (PCA) has been applied to many industrial chemical processes. However, such PCA-based approaches assume that the process is operating in a steady state and consequently that the process data are normally distributed and contain no time correlations. These assumptions significantly limit the applicability of PCA-based approaches to the monitoring of real processes. In this paper, we propose a more exact and realistic process monitoring method that does not require that the process data be normally distributed. Specifically, the concept of conventional PCA is expanded such that a Gaussian mixture model (GMM) is used to approximate the data pattern in the model subspace obtained by PCA. The use of a mixture of local Gaussian models means that the proposed approach can be applied to arbitrary datasets, not just those showing a normal distribution. To use the GMM for monitoring, the overall T-2 and Q statistics were used as the monitoring guidelines for fault detection. The proposed approach significantly relaxes the restrictions inherent in conventional PCA-based approaches in regard to the raw data pattern, and can be expanded to dynamic process monitoring without developing a complicated dynamic model. In addition, a GMM via discriminant analysis is proposed to isolate faults. The proposed monitoring method was successfully applied to three case studies: (1) simple two-dimensional toy problems, (2) a simulated 4 x 4 dynamic process, and (3) a simulated non-isothermal continuous stirred tank reactor (CSTR) process. These application studies demonstrated that, in comparison to conventional PCA-based monitoring, the proposed fault detection and isolation (FDI) scheme is more accurate and efficient. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： principal component analysis Gaussian mixture model expectation-maximization algorithm overall T-2 discriminant analysis ARL

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A human-robot cooperative learning system for easy installation of assistant robots in new working environments

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2004年第3期40卷 233-265页

作者： López, ME Barea, R Bergasa, LM Escudero, MS Univ Alcala De Henares Dept Elect Alcala De Henares Spain

One of the applications of service robots with a greater social impact is the assistance to elderly or disabled people. In these applications, assistant robots must robustly navigate in structured indoor environments such as hospitals, nursing homes or houses, heading from room to room to carry out different nursing or service tasks. Among the main requirements of these robotic aids, one that will determine its future commercial feasibility, is the easy installation of the robot in new working domains without long, tedious or complex configuration steps. This paper describes the navigation system of the assistant robot called SIRA, developed in the Electronics Department of the University of Alcala, focusing on the learning module, specially designed to make the installation of the robot easier and faster in new environments. To cope with robustness and reliability requirements, the navigation system uses probabilistic reasoning (POMDPs) to globally localize the robot and to direct its goal-oriented actions. The proposed learning module fast learns the Markov model of a new environment by means of an exploration stage that takes advantage of human - robot interfaces ( basically speech) and user - robot cooperation to accelerate model acquisition. The proposed learning method, based on a modification of the EM algorithm, is able to robustly explore new environments with a low number of corridor traversals, as shown in some experiments carried out with SIRA.

关键词： probabilistic navigation partially observable Markov decision processes learning under uncertainty expectation-maximization algorithm assistant robots

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