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检索条件"主题词=Expectation-maximisation algorithm"

共 493 条记录，以下是241-250 订阅

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EM Reconstruction of Dual Isotope PET with Staggered Injections and Prompt Gamma Positron Emitters

EM Reconstruction of Dual Isotope PET with Staggered Injecti...

引用

IEEE Nuclear Science Symposium / Medical Imaging Conference Record (NSS/MIC) / 19th Room-Temperature Semiconductor X-ray and Gamma-ray Detector Workshop

作者： Andreyev, Andriy Sitek, Arkadiusz Celler, Anna Univ British Columbia Med Imaging Res Grp Vancouver BC V5Z 1M9 Canada Brigham & Womens Hosp Harvard Med Sch Dept Radiol Boston MA 02115 USA

ISBN: (纸本)9781467320306;9781467320283

The aim of Dual Isotope Positron Emission Tomography (DIPET) is to create two independent images of two co-injected radiotracers. DIPET shortens the duration of the study, reduces patient discomfort and produces perfectly co-registered images. Its implementation is difficult because positron decay of any isotope creates only 511 keV photons. Recently, we have proposed a DIPET technique that uses a combination of radiotracer A which is a pure positron emitter (such as F-18 or C-11) and radiotracer B in which positron decay is followed by the emission of a high-energy (HE) prompt gamma (such as K-38 or Cu-60). Events that are detected as triple coincidences of HE gammas with the corresponding two 511 keV photons identify the line-of-responses (LORs) that originate from isotope B (similar to 10% of all events). These LORs are used to separate the two intertwined distributions, using a dedicated image reconstruction algorithm based on expectation Maximization (EM). However, this approach is suboptimal as it combines high-statistics data (radiotracers A and B), with low-statistics (radiotracer B) to create separate images of radiotracers A and B. We hypothesize that from the performance of DIPET could be improved if an independent estimate of radiotracer A distribution is available by using staggered injections method. Here we investigate this hypothesis by developing and testing an EM-based algorithm for image reconstruction of such data. Our algorithm is general, i.e. can reconstruct staggered data, positron and HE gamma data or a combination of both. Reconstruction of DIPET data obtained from Monte-Carlo simulations shows that the combined method provides the best results in terms of separation of images corresponding to radiotracers A and B. Our EM algorithm provides not only good image separation for the combined method but also when only staggered injections or only prompt gamma data are used.

关键词： Monte Carlo methods expectation-maximisation algorithm image reconstruction medical image processing positron emission tomography

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On Improving the Extrapolation Capability of Task-Parameterized Movement Models

On Improving the Extrapolation Capability of Task-Parameteri...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Calinon, Sylvain Alizadeh, Tohid Caldwell, Darwin G. IIT Dept Adv Robot I-16163 Genoa Italy

ISBN: (纸本)9781467363587

Gestures are characterized by intermediary or final landmarks (real or virtual) in task space or joint space that can change during the course of the motion, and that are described by varying accuracy and correlation constraints. Generalizing these trajectories in robot learning by imitation is challenging, because of the small number of demonstrations provided by the user. We present an approach to statistically encode movements in a task-parameterized mixture model, and derive an expectation-maximization (EM) algorithm to train it. The model automatically extracts the relevance of candidate coordinate systems during the task, and exploits this information during reproduction to adapt the movement in real-time to changing position and orientation of landmarks or objects. The approach is tested with a robotic arm learning to roll out a pizza dough. It is compared to three categories of task-parameterized models: 1) Gaussian process regression (GPR) with a trajectory models database;2) Multi-streams approach with models trained in several frames of reference;and 3) Parametric Gaussian mixture model (PGMM) modulating the Gaussian centers with the task parameters. We show that the extrapolation capability of the proposed approach outperforms existing methods, by extracting the local structures of the task instead of relying on interpolation principles.

关键词： Gaussian processes adaptive control expectation-maximisation algorithm extrapolation learning by example learning systems manipulators motion control regression analysis trajectory control

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Sound Event Detection in Remote Health Care - Small Learning Datasets and Over Constrained Gaussian Mixture Models

Sound Event Detection in Remote Health Care - Small Learning...

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32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)

作者： Montalvao, Jugurta Istrate, Dan Boudy, Jerome Mouba, Joan Univ Sergipe Fac Elect Engn BR-49100000 Sao Cristovao Brazil ESIGETEL Sch F-77210 Fontainebleau France Telecom Paris F-91011 Evry France

ISBN: (纸本)9781424441242

The use of Gaussian Mixture Models (GMM), adapted through the expectation Minimization(EM) algorithm, is not rare in Audio Analysis for Surveillance Applications and Environmental sound recognition. Their use is founded on the good qualities of GMM models when aimed at approximating Probability Density Functions (PDF) of random variables. But in some cases, where models are to be adapted from small sample sets instead of large but generic databases, a problem of balance between model complexity and sample size may play an important role. From this perspective, we show, through simple sound classification experiments, that constrained GMM, with fewer degrees of freedom, as compared to GMM with full covariance matrices, provide better classification performances. Moreover, pushing this argument even further, we also show that a Parzen model can do even better than usual GMM.

关键词： Gaussian mixture models Parzen model acoustic signal detection audio analysis audio signal processing expectation minimization algorithm expectation-maximisation algorithm health care learning (artificial intelligence) learning dataset medical signal processing model complexity over constrained GMM patient monitoring probability density functions random variable PDF remote health care sample size sound classification experiments sound event detection telemedicine

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THE LINEAR PROCESS MIXTURE MODEL

THE LINEAR PROCESS MIXTURE MODEL

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23rd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

作者： Palmer, Jason A. Kreutz-Delgado, Ken Makeig, Scott Univ Calif San Diego Swartz Ctr Computat Neurosci La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA

ISBN: (纸本)9781479911806

We consider a likelihood framework for analyzing multivariate time series as mixtures of independent linear processes. We propose a flexible, Newton algorithm for estimating impulse response functions associated with independent linear processes and an EM-based finite mixture model to handle intermittent regimes. Simulations and application to EEG are also provided.

关键词： Newton method electroencephalography expectation-maximisation algorithm medical signal processing time series

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An EM based training algorithm for cross-language text categorization 05

An EM based training algorithm for cross-language text categ...

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IEEE/WIC/ACM International Conference on Web Intelligence

作者： Rigutini, L Maggini, M Liu, B Univ Siena Dipartimento Ingn Informaz I-53100 Siena Italy

ISBN: (纸本)076952415X

Due to the globalization on the Web, many companies and institutions need to efficiently organize and search repositories containing multilingual documents. The management of these heterogeneous text collections increases the costs significantly because experts of different languages are required to organize these collections. Cross-Language Text Categorization can provide techniques to extend existing automatic classification systems in one language to new languages without requiring additional intervention of human experts. In this paper we propose a learning algorithm based on the EM scheme which can be used to train text classifiers in a multilingual environment. In particular in the proposed approach, we assume that a predefined category set and a collection of labeled training data is available for a given language L-1. A classifier for a different language L-2 is trained by translating the available labeled training set for L-1 to L-2 and by using an additional set of unlabeled documents from L-2. This technique allows us to extract correct statistical properties of the language L-2 which are not completely available in automatically translated examples, because of the different characteristics of language L-1 and of the approximation of the translation process. Our experimental results show that the performance of the proposed method is very promising when applied on a test document set extracted from newsgroups in English and Italian.

关键词： classification expectation-maximisation algorithm learning (artificial intelligence) natural languages text analysis EM based training algorithm cross-language text categorization learning algorithm multilingual documents search repositories

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Unsupervised model based SAR data denoising

Unsupervised model based SAR data denoising

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IEEE International Symposium on Industrial Electronics

作者： Gleich, D Datcu, M German Aerosp Ctr D-82234 Wessling Germany

ISBN: (纸本)0780387384

In this paper a wavelet based method for SAR data denoising is presented. The SAR image is corrupted by a multiplicative noise that can be modeled as an additive noise in wavelet domain. In this paper an image is modeled as a Gauss Markov Random field and noise is considered as Gaussian with unknown variance. An unsupervised stochastic model based approach to image denoising is presented. The parameters are estimated from incomplete data using mixtures of wavelet coefficients, and expectation maximization algorithm. Observed wavelet coefficient is estimated using inter and intra scale of wavelet coefficients to estimate image and noise model parameters. Presented wavelet based method efficiently removes noise from SAR images.

关键词： Gaussian noise Markov processes data communication expectation-maximisation algorithm image denoising radar imaging synthetic aperture radar wavelet transforms Gauss Markov random field SAR data denoising SAR image additive noise expectation maximization

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A Multi-Objective Genetic Graph-based Clustering algorithm with Memory Optimization

A Multi-Objective Genetic Graph-based Clustering Algorithm w...

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IEEE Congress on Evolutionary Computation

作者： Menendez, Hector D. Barrero, David F. Camacho, David Univ Autonoma Madrid Dept Comp Sci E-28049 Madrid Spain Univ Alcala de Henares Dept Automat E-28871 Alcala De Henares Spain

ISBN: (纸本)9781479904549;9781479904532

Clustering is one of the most versatile tools for data analysis. Over the last few years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the Spectral Clustering algorithm, which is based on graph cut: it initially generates a Similarity Graph using a distance measure and then uses its Graph Spectrum to find the best cut. Memory consuption is a serious limitation in that algorithm: The Similarity Graph representation usually requires a very large matrix with a high memory cost. This work proposes a new algorithm, based on a previous implementation named Genetic Graph-based Clustering (GGC), that improves the memory usage while maintaining the quality of the solution. The new algorithm, called Multi-Objective Genetic Graph-based Clustering (MOGGC), uses an evolutionary approach introducing a Multi-Objective Genetic algorithm to manage a reduced version of the Similarity Graph. The experimental validation shows that MOGGC increases the memory efficiency, maintaining and improving the GGC results in the synthetic and real datasets used in the experiments. An experimental comparison with several classical clustering methods (EM, SC and K-means) has been included to show the efficiency of the proposed algorithm.

关键词： data analysis data mining expectation-maximisation algorithm genetic algorithms graph theory learning (artificial intelligence) pattern clustering

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Estimating Multiple Concurrent Processes

Estimating Multiple Concurrent Processes

引用

IEEE International Symposium on Information Theory

作者： Acharya, Jayadev Das, Hirakendu Jafarpour, Ashkan Orlitsky, Alon Pan, Shengjun Univ Calif San Diego ECE San Diego CA USA

ISBN: (纸本)9781467325790

We consider two related problems of estimating properties of a collection of point processes: estimating the multiset of parameters of continuous-time Poisson processes based on their activities over a period of time t, and estimating the multiset of activity probabilities of discrete-time Bernoulli processes based on their activities over n time instants. For both problems, it is sufficient to consider the observations' profile-the multiset of activity counts, regardless of their process identities. We consider the profile maximum likelihood (PML) estimator that finds the parameter multiset maximizing the profile's likelihood, and establish some of its competitive performance guarantees. For Poisson processes, if any estimator approximates the parameter multiset to within distance epsilon with error probability delta, then PML approximates the multiset to within distance 2 delta with error probability at most delta . e(4)root(t.S), where S is the sum of the Poisson parameters, and the same result holds for Bernoulli processes. In particular, for the L-1 distance metric, we relate the problems to the long- studied distribution-estimation problem and apply recent results to show that the PML estimator has error probability e(-(t.S)0.9) for Poisson processes whenever the number of processes is k = O(tS log(tS)), and show a similar result for Bernoulli processes. We also show experimental results where the EM algorithm is used to compute the PML.

关键词： Poisson process Error probability Bernoulli expectation-maximisation algorithm concurrent process

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Learning Outcome-Discriminative Dynamics in Multivariate Physiological Cohort Time Series

Learning Outcome-Discriminative Dynamics in Multivariate Phy...

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35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Nemati, Shamim Lehman, Li-wei H. Adams, Ryan P. Harvard Univ Sch Engn & Appl Sci 33 Oxford St Cambridge MA 02138 USA

ISBN: (纸本)9781457702167

Model identification for physiological systems is complicated by changes between operating regimes and measurement artifacts. We present a solution to these problems by assuming that a cohort of physiological time series is generated by switching among a finite collection of physiologically-constrained dynamical models and artifactual segments. We model the resulting time series using the switching linear dynamical systems (SLDS) framework, and present a novel learning algorithm for the class of SLDS, with the objective of identifying time series dynamics that are predictive of physiological regimes or outcomes of interest. We present exploratory results based on a simulation study and a physiological classification example of decoding postural changes from heart rate and blood pressure. We demonstrate a significant improvement in classification over methods based on feature learning via expectation maximization. The proposed learning algorithm is general, and can be extended to other applications involving state-space formulations.

关键词： cardiology expectation-maximisation algorithm learning systems medical signal processing multivariable systems physiology signal classification time series

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Radar High Resolution Range Profile Target Recognition Based on T-mixture model

Radar High Resolution Range Profile Target Recognition Based...

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IEEE Radar Conference (RADAR)

作者： Wang, Caiyun Xie, Jiling Nanjing Univ Aeronaut & Astronaut Acad Frontier Sci Nanjing 210016 Peoples R China Nanjing Res Inst Elect Technol Nanjing 210039 Jiangsu Peoples R China

ISBN: (纸本)9781424489022

This paper develops a T mixture model (TMM) approach to automatic radar target recognition. The specific problem considered is the classification of radar high-resolution range profile (HRRP). We model the radar returns from each target as a t-mixture distribution. Estimation of the model parameters is achieved using a modified expectation-maximization (EM), i.e., fuzzy EM (FEM) algorithm. Experimental results demonstrate the proposed statistical model not only has better recognition performance but also is more robust to noises than other methods.

关键词： Clustering algorithms Computational modeling Estimation Radar Robustness Signal to noise ratio T-mixture distribution T-mixture model Target recognition automatic radar target recognition classification expectation-maximisation algorithm fuzzy EM algorith

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