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

共 493 条记录，以下是361-370 订阅

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MATCHING NON-ALIGNED OBJECTS USING A RELATIONAL STRING-GRAPH

MATCHING NON-ALIGNED OBJECTS USING A RELATIONAL STRING-GRAPH

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IEEE International Conference on Image Processing

作者： Nicholas Dahm Yongsheng Gao Terry Caelli Horst Bunke Queensland Research Laboratory National ICT Australia School of Engineering Griffith University Brisbane Australia Victoria Research Laboratory National ICT Australia Institute of Computer Science and Applied Mathematics University of Bern Switzerland

ISBN: (纸本)9781479923427

Localising and aligning objects is a challenging task in computer vision that still remains largely unsolved. Utilising the syntactic power of graph representation, we define a relational string-graph matching algorithm that seeks to perform these tasks simultaneously. By matching the relations between vertices, where vertices represent high-level primitives, the relational string-graph is able to overcome the noisy and inconsistent nature of the vertices themselves. For each possible relation correspondence between two graphs, we calculate the rotation, translation, and scale parameters required to transform a relation into its counterpart. We plot these parameters in 4D space and use Gaussian mixture models and the expectation-maximisation algorithm to estimate the underlying parameters. Our method is tested on face alignment and recognition, but is equally (if not more) applicable for generic object alignment.

关键词： image alignment face recognition graph matching image registration Graph matching Facial Recognition images computer vision expectation-maximisation algorithm Graph representations vertex head scale parameter

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Modelling temporal evolution of cardiac electrophysiological features using Hidden Semi-Markov Models

Modelling temporal evolution of cardiac electrophysiological...

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

作者： Jerome Dumont Alfredo I. Hernandez Julien Fleureau Guy Carrault Universite de Rennes 1 LTSI Rennes France INSERM Rennes France

ISBN: (纸本)9781424418145

This paper presents a new method to analyse cardiac electrophysiological dynamics. It aims to classify or to cluster (i.e. to find natural groups) patients according to the dynamics of features extracted from their ECG. In this work, the dynamics of the features are modelled with Continuous Density Hidden Semi-Markovian Models (CDHSMM) which are interesting for the characterization of continuous multivariate time series without a priori information. These models can be easily used for classification and clustering. In this last case, a specific method, based on a fuzzy expectation maximisation (EM) algorithm, is proposed. Both tasks are applied to the analysis of ischemic episodes with encouraging results and a classification accuracy of 71%.

关键词： Heart Electrocardiography electrochemical grinding Religious Missions expectation-maximisation algorithm dynamics Time series

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MMSE-BASED SOURCE EXTRACTION USING POSITION-BASED POSTERIOR PROBABILITIES

MMSE-BASED SOURCE EXTRACTION USING POSITION-BASED POSTERIOR ...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Maja Taseska Emanuel A.P. Habets International Audio Laboratories Erlangen

ISBN: (纸本)9781479903573

A scenario with multiple talkers and additive background noise is considered, where some talkers are active simultaneously and the activity of the talkers changes with time. We propose an MMSE-based method to blindly extract any talker using bin-wise position estimates obtained from distributed microphone arrays. In order to distinguish between different talkers, the position estimates are clustered using the expectation maximization algorithm. The resulting posterior probabilities allow to estimate the PSD matrices of the talkers and compute an MMSE-optimal linear filter for extracting each talker. We evaluate the performance of the proposed method in terms of noise and interference reduction and distortion of the desired speech signal at the output of a multichannel Wiener filter.

关键词： Speech separation PSD matrix estimation Distributed arrays expectation maximization Wiener filters Voice Signal BACKGROUND NOISE disturbance suppression microphone arrays expectation-maximisation algorithm linear filters

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An expectation Maximization algorithm for Textual Unit Alignment 11

An Expectation Maximization Algorithm for Textual Unit Align...

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Approaches and Methods for Integrating Geological Information in The Safety Case Workshop on Geological Disposal: Building Confidence Using Multiple Lines of Evidence

作者： Radu Ion Alexandru Ceausu Elena Irimia Research Institute for AI Calea 13 Septembrie nr. 13 Bucharest 050711 Romania

ISBN: (纸本)9781618392497

The paper presents an expectation Maximization (EM) algorithm for automatic generation of parallel and quasi-parallel data from any degree of comparable corpora ranging from parallel to weakly comparable. Specifically, we address the problem of extracting related textual units (documents, paragraphs or sentences) relying on the hypothesis that, in a given corpus, certain pairs of translation equivalents are better indicators of a correct textual unit correspondence than other pairs of translation equivalents. We evaluate our method on mixed types of bilingual comparable corpora in six language pairs, obtaining state of the art accuracy figures.

关键词： expectation-maximisation algorithm autogeneration Paragraphs state of the art Comparable Parallel Lines

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Model choice for binned-EM algorithms of fourteen parsimonious Gaussian mixture models by BIC and ICL criteria

Model choice for binned-EM algorithms of fourteen parsimonio...

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IEEE International Conference on System Science and Engineering

作者： Jingwen Wu Hani Hamdan SUPELEC Department of Signal Processing and Electronic Systems

ISBN: (纸本)9781479900084

Choosing the right model is an important step in model-based clustering approaches. In this framework, BIC and ICL criteria were proposed to choose a model for clustering of standard data. On the other hand, in order to accelerate the data processing when using EM algorithm, this algorithm was adapted to binned data (binned-EM algorithm). Then fourteen binned-EM algorithms of fourteen parsimonious Gaussian mixture models were developed to replace the binned-EM algorithm of the most general Gaussian mixture model when data have a simple structure. So this paper studies the application of BIC and ICL criteria to select a good model which better fits binned data, when clustering is based on these fourteen binned-EM algorithms. Numerical experiments on simulated and real data are performed, and the experimental results are analyzed.

关键词： expectation-maximisation algorithm imidazole mustard algorithms Cluster Analysis simple structures Mixture models Fourteen

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Relaxation of the EM algorithm via quantum annealing

Relaxation of the EM algorithm via quantum annealing

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American Control Conference

作者： Hideyuki Miyahara Koji Tsumura Department of Information Physics and Computing the Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9781467386838

The EM algorithm is a novel numerical method to obtain maximum likelihood estimates and is often used for practical calculations. However, many of maximum likelihood estimation problems are nonconvex, and it is known that the EM algorithm fails to give the optimal estimates by being trapped by local optima. In order to deal with this difficulty, we propose a deterministic quantum annealing EM algorithm by introducing the mathematical mechanism of quantum fluctuations into the conventional EM algorithm because quantum fluctuations induce the tunnel effect and are expected to relax the difficulty of nonconvex optimization problems in the maximum likelihood estimation problems. We show a theorem that guarantees its convergence and give numerical experiments to verify its efficiency.

关键词： Annealing Maximum likelihood estimation Convergence Probability density function Numerical simulation Simulated annealing expectation-maximisation algorithm maximum likelihood estimation annealing Probability density function simulated annealing Converge numerical simulation Tunnel effect quanta

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Application of Probabilistic Modeling and Machine Learning to the Diagnosis of FTTH GPON Networks

Application of Probabilistic Modeling and Machine Learning t...

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International Conference on Optical Network Design and Modelling

作者： Stéphane Gosselin Jean-Luc Courant Serge Romaric Tembo Sandrine Vaton Orange Labs Lannion France ActiveEon Sophia Antipolis France IMT Atlantique Brest France

ISBN: (纸本)9781509040063

This paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access Network self-DiAgnosis (PANDA), is applied to fault diagnosis of Gigabit capable Passive Optical Networks (GPON). PANDA approach has been assessed on real diagnosis data, showing very satisfactory alignment with an operational rule-based expert system. Furthermore, it provides diagnosis conclusions for all tested cases, even if some monitoring data is missing or incomplete. Finally, an expectation maximization algorithm allows to finely tune the probabilistic model.

关键词： Passive optical networks Expert systems Probabilistic logic Bayes methods Fault diagnosis Optical variables measurement Fault diagnosis Passive optical networks Expert System Optical variables measurement Probabilistic logic Bayes methods Probabilistic Model Gigabit passive optical network access network Probability expectation-maximisation algorithm PANDA PANDAR gene Artificial Intelligence Machine Learning

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A geometric learning approach on the space of complex covariance matrices

A geometric learning approach on the space of complex covari...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hatem Hajri Salem Said Lionel Bombrun Yannick Berthoumieu Laboratoire IMS (CNRS-UMR 5218) Universite de Bordeaux France

ISBN: (纸本)9781509041183

Many signal and image processing applications, including SAR polarimetry and texture analysis, require the classification of complex covariance matrices. The present paper introduces a geometric learning approach on the space of complex covariance matrices based on a new distribution called Riemannian Gaussian distribution. The proposed distribution has two parameters, the centre of mass Y and the dispersion parameter σ. After having derived its maximum likelihood estimator and its extension to mixture models, we propose an application to texture recognition on the VisTex database.

关键词： Classification Hermitian matrices Information geometry Riemannian centre of mass EM algorithm Information geometry Hermitian matrix variance covariance matrix expectation-maximisation algorithm Covariance matrix New records maximum likelihood estimation texture analysis

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A dynamic segmentation based activity discovery through topic modelling

A dynamic segmentation based activity discovery through topi...

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IET International Conference on Technologies for Active and Assisted Living (TechAAL)

作者： Ihianle Isibor Kennedy Usman Naeem Abdel-rahman Tawil University of East London London London GB

Recent developments in ubiquitous and pervasive technologies have made it easier to capture activities through sensors. The “bag-of-word” topic models have been applied to discover latent topics in corpus of words. In this paper, we propose the Probabilistic Latent Semantic Analysis to discover activity routines. The framework we propose set latent topics as corresponding class labels and use the expectation Maximization (EM) algorithm for posterior inference. The experimental results we present are based on the Kasteren dataset which validates our framework and shows that it is comparable to existing activity discovery approaches.

关键词： Activity Discovery Bag-of-word Probabilistic Latent Semantic Analysis (PLSA) Sensor Segments Topic Model activity Frameworks Themes latent semantic analysis expectation-maximisation algorithm Modeling

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Parametric Density Estimation Using EM algorithm for Collaborative Spectrum Sensing

Parametric Density Estimation Using EM Algorithm for Collabo...

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3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008)

作者： Shun-Te Tseng Han-Ting Chiang James S. Lehnert School of Electrical and Computer Engineering Purdue University Calumet West Lafayette IN USA

ISBN: (纸本)9781424423019

Collaborative sensing of spectral occupancy can increase accuracy and relax the required sensitivity of individual sensing units. Collaborative sensing requires knowledge about the densities of collected sensing statistics to form the correct decision statistics for the optimum likelihood ratio test. In this paper, a parametric density estimation scheme using the expectation-maximization (EM) algorithm is proposed to estimate the parameters of densities that are drawn from a given family. When the log-likelihood function for the EM algorithm satisfies a certain condition, the maximization procedure is shown to require only a weighted sum of the collected sensing statistics. Numerical examples show that in various scenarios the proposed EM algorithm produces more accurate estimates than the sample average does.

关键词： expectation-maximisation algorithm likelihood ratio test Sample mean log-likelihood function Parametric

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