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

共 495 条记录，以下是441-450 订阅

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Learning a Mixture of Sparse Models by EM algorithm for Object Clustering

Learning a Mixture of Sparse Models by EM Algorithm for Obje...

引用

International Conference on Computer Science and Network Technology

作者： Yuhan Fang Ruojing Jiang Chenguang Li College of Electronic and Information Engineering Southwest University Chongqing China School of Mathematical Sciences Fudan University Shanghai China Department of Information Engineering Hefei University of Technology Xuancheng China

ISBN: (纸本)9781467381741

Model-based object clustering is a very challenging unsupervised learning problem in computer vision, which involves both high dimensionality and hidden variables inference issues. In this paper, we will study object pattern clustering problem by using the Active basis model, which is a sparse representation model for object patterns. We fit a mixture of active basis models, which leads to an object clustering result, as well as the inference of all the hidden variables in the object clustering problem. This strategy not only gives us a way to represent each object by a sparse model, but also an elegant solution to the clustering problem with hidden variables, such as unknown locations, scales, and orientations of the objects appearing in the images. The experiment conducted on a small clustering dataset shows that learning a mixture of active basis models by EM-like algorithm for object clustering is very promising.

关键词： Object clustering Active basis model EM algorithm Computer vision Mixture model expectation-maximisation algorithm images computer vision Cluster Analysis Mixture models mixtures HIDDEN VARIABLES sparse representation

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A Novel OFDM Power Based Estimation for Dynamic Channel Tracking in Downlink LTE

A Novel OFDM Power Based Estimation for Dynamic Channel Trac...

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IEEE Vehicular Technology Conference

作者： Ali Kalakech Loic Brunel Marion Berbineau David Mottier Univ Lille Nord de France F-59000 Lille IFSTTAR LEOST Mitsubishi Electric R & D Center Europe

ISBN: (纸本)9781467318808

In this paper we compare several channel estimation algorithms in the case of LTE (Long Term Evolution) System when applied in a high mobility environment. In particular, we propose a novel algorithm, based on the observation of OFDM block energy, to perform a semi-blind tracking of the channel between pilot blocks. This algorithm is compared to more traditional approaches based on ID estimation and interpolation. In addition, an expectation Maximization (EM) algorithm is used to improve the overall channel estimation.

关键词： channel estimation Long Term Evolution Local thermodynamic equilibrium dynamic channel Orthogonal frequency division multiplexing Downlink Pilots Interpolation tracking expectation-maximisation algorithm

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Unified expectation Maximization 12

Unified Expectation Maximization

引用

Conference of the North American Chapter of the Association for Computational Linguistics: human language technologies

作者： Rajhans Samdani Ming-Wei Chang Dan Roth University of Illinois Microsoft Research

ISBN: (纸本)9781937284206

We present a general framework containing a graded spectrum of expectation Maximization (EM) algorithms called Unified expectation Maximization (UEM.) UEM is parameterized by a single parameter and covers existing algorithms like standard EM and hard EM, constrained versions of EM such as Constraint-Driven Learning (Chang et al., 2007) and Posterior Regularization (Ganchev et al., 2010), along with a range of new EM algorithms. For the constrained inference step in UEM we present an efficient dual projected gradient ascent algorithm which generalizes several dual decomposition and Lagrange relaxation algorithms popularized recently in the NLP literature (Ganchev et al., 2008;Koo et al., 2010;Rush and Collins, 2011). UEM is as efficient and easy to implement as standard EM. Furthermore, experiments on POS tagging, information extraction, and word-alignment show that often the best performing algorithm in the UEM family is a new algorithm that wasn't available earlier, exhibiting the benefits of the UEM framework.

关键词： algorithms Information Extraction aluminium expectation-maximisation algorithm Lagrangian

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Parameter Estimation over Wired Networks with Time-Delay and Packet Collision

Parameter Estimation over Wired Networks with Time-Delay and...

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Felipe Arancibia Rodrigo Carvajal Juan C. Agüero Departamento de Electrónica Universidad Técnica Federico Santa María Chile

ISBN: (纸本)9781728131863

In this paper, we address a parameters estimation problem of a dynamic system using an Output Error model, in which the output signal is transmitted through wired networks. We consider that the wired network may cause unknown delays and collision packet in data. We use the Akaike information criterion to estimate the time delay caused by wired network. Furthermore, due to the packet loss caused by wired network, we use expectation Maximization algorithm to estimate the dynamic system parameters. Finally, we test the proposed algorithm effectiveness with numerical examples and a sensitivity analysis.

关键词： delays expectation-maximisation algorithm parameter estimation sensitivity analysis expectation-maximisation algorithm Parameter estimation wired network Sensitivity analysis time delay Dynamic systems packet collision outgoing signal delay error model

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Multi-view Anomaly Detection via Robust Probabilistic Latent Variable Models 16

Multi-view Anomaly Detection via Robust Probabilistic Latent...

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Annual Conference on Neural Information Processing Systems

作者： Tomoharu Iwata Makoto Yamada NTT Communication Science Laboratories Kyoto University

ISBN: (纸本)9781510838819

We propose probabilistic latent variable models for multi-view anomaly detection, which is the task of finding instances that have inconsistent views given multi-view data. With the proposed model, all views of a non-anomalous instance are assumed to be generated from a single latent vector. On the other hand, an anomalous instance is assumed to have multiple latent vectors, and its different views are generated from different latent vectors. By inferring the number of latent vectors used for each instance with Dirichlet process priors, we obtain multiview anomaly scores. The proposed model can be seen as a robust extension of probabilistic canonical correlation analysis for noisy multi-view data. We present Bayesian inference procedures for the proposed model based on a stochastic EM algorithm. The effectiveness of the proposed model is demonstrated in terms of performance when detecting multi-view anomalies.

关键词： anomaly detection Information retrieval systems-Teletext and videotex Probability expectation-maximisation algorithm View Inconsistent Variable

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Latent tree approximation in linear model

Latent tree approximation in linear model

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Navid Tafaghodi Khajavi Dept. of Electrical Engineering University of Hawaii Honolulu 96822 United States of America

ISBN: (纸本)9781509041183

We consider the problem of learning underlying tree structure from noisy, mixed data obtained from a linear model. To achieve this, we use the expectation maximization algorithm combined with Chow-Liu minimum spanning tree algorithm. This algorithm is sub-optimal, but has low complexity and is applicable to model selection problems through any linear model.

关键词： Tree Approximation EM algorithm expectation-maximisation algorithm linear models TREE STRUCTURES model selection spanning tree algorithm approximation

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Bayesian Anisotropic Gaussian Model for Audio Source Separation

Bayesian Anisotropic Gaussian Model for Audio Source Separat...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Paul Magron Tuomas Virtanen Laboratory of Signal Processing Tampere University of Technology Finland

ISBN: (纸本)9781538646595

In audio source separation applications, it is common to model the sources as circular-symmetric Gaussian random variables, which is equivalent to assuming that the phase of each source is uniformly distributed. In this paper, we introduce an anisotropic Gaussian source model in which both the magnitude and phase parameters are modeled as random variables. In such a model, it becomes possible to promote a phase value that originates from a signal model and to adjust the relative importance of this underlying model-based phase constraint. We conduct Bayesian inference of the model through the derivation of an expectation-maximization algorithm for estimating the parameters. Experiments conducted on realistic music songs for a monaural source separation task, in an scenario where the variance parameters are assumed known, show that the proposed approach outperforms state-of-the-art techniques.

关键词： Anisotropic Gaussian model Bayesian inference expectation-maximization audio source separation Source separation Bayesian inference Gauss Random variable Audio Phase expectation-maximisation algorithm Anisotropy

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High Dimensional EM algorithm: Statistical Optimization and Asymptotic Normality 15

High Dimensional EM Algorithm: Statistical Optimization and ...

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Annual Conference on Neural Information Processing Systems

作者： Zhaoran Wang Quanquan Gu Yang Ning Han Liu Princeton University University of Virginia

ISBN: (纸本)9781510825024

We provide a general theory of the expectation-maximization (EM) algorithm for inferring high dimensional latent variable models. In particular, we make two contributions: (ⅰ) For parameter estimation, we propose a novel high dimensional EM algorithm which naturally incorporates sparsity structure into parameter estimation. With an appropriate initialization, this algorithm converges at a geometric rate and attains an estimator with the (near-)optimal statistical rate of convergence, (ⅱ) Based on the obtained estimator, we propose a new inferential procedure for testing hypotheses for low dimensional components of high dimensional parameters. For a broad family of statistical models, our framework establishes the first computationally feasible approach for optimal estimation and asymptotic inference in high dimensions.

关键词： expectation-maximisation algorithm DIMENSIONAL Parameter estimation optimal estimation Hypothesis testing statistical models (nuclear) normality Estimators asymptotic

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NOVEL BAYESIAN CLUSTER ENUMERATION CRITERION FOR CLUSTER ANALYSIS WITH FINITE SAMPLE PENALTY TERM

NOVEL BAYESIAN CLUSTER ENUMERATION CRITERION FOR CLUSTER ANA...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Freweyni K. Teklehaymanot Michael Muma Abdelhak M. Zoubir Signal Processing Group Technische Universit?t Darmstadt Merckstra?e 25 64283 Darmstadt Germany

ISBN: (纸本)9781538646595

The Bayesian information criterion is generic in the sense that it does not include information about the specific model selection problem at hand. Nevertheless, it has been widely used to estimate the number of data clusters in cluster analysis. We have recently derived a Bayesian cluster enumeration criterion from first principles which maximizes the posterior probability of the candidate models given observations. But, in the finite sample regime, the asymptotic assumptions made by the criterion, to arrive at a computationally simple penalty term, are violated. Hence, we propose a Bayesian cluster enumeration criterion whose penalty term is derived by removing the asymptotic assumptions. The proposed algorithm is a two-step approach which uses a model-based clustering algorithm such as the EM algorithm before applying the derived criterion. Simulation results demonstrate the superiority of our criterion over existing Bayesian cluster enumeration criteria.

关键词： Cluster Enumeration Bayesian Information Criterion Cluster Analysis Small Sample Sizes Bayesian information criteria Cluster Analysis penalty expectation-maximisation algorithm Bayesian inference a posteriori probability model selection

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A maximum likelihood approach to state estimation of complex dynamical networks with unknown noisy transmission channel

A maximum likelihood approach to state estimation of complex...

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IEEE International Symposium on Circuits and Systems

作者： Hao Zhu Henry Leung Department of Automation Chongqing University of Posts and Telecommunications Chongqing China Department of Electrical and Computer Engineering University of Calgary Calgary Alberta Canada

ISBN: (纸本)9781467357609

In this paper, the problem of state estimation of complex dynamical network with unknown noisy transmission channel is considered. A likelihood function of the complex network is formulated. An expectation maximum (EM) algorithm is proposed to estimate the complex network's state and the noise parameter simultaneously. The proposed method can obtain suboptimal estimate of the state and noise parameter. At each iteration of the EM algorithm, the complex network's state is estimated by extend Kalman filter in the E-step, while the noise parameter is updated in the M-step. Computer simulation results verify the effectiveness of the proposed method.

关键词： state estimation noisy channel expectation maximum extend Kalman filter State estimation Noise parameters noisy channel maximum likelihood Extended Kalman filters dynamic network complex networks Transmission channels Likelihood Functions expectation-maximisation algorithm

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