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检索条件"主题词=Expectation Maximization Algorithm"

共 643 条记录，以下是351-360 订阅

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Unsupervised Color Image Segmentation by Clustering into Multivariate Gaussians

Unsupervised Color Image Segmentation by Clustering into Mul...

引用

5th International Conference on Information Processing (ICIP 2011)

作者： Raj, Jobin Govindan, V. K. Natl Inst Technol Calicut Dept Comp Sci & Engn Calicut Kerala India

ISBN: (纸本)9783642227851

An Unsupervised Color Image Segmentation algorithm by using Finite Gaussian Mixture Model (GMM) is proposed in this paper. The parameters of GMM are estimated using expectation maximization (EM) algorithm. A novel technique for initializing the EM algorithm is presented. The algorithm runs in two stages. The first stage performs an initial segmentation which initializes the EM algorithm. The second stage is the parameter estimation phase using EM algorithm. The scheme is computationally efficient in terms of space and time complexity.

关键词： expectation maximization algorithm Gaussian Mixture Model Image Segmentation Model-based Segmentation Multivariate Gaussian Distribution

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UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dupraz, Elsa Roumy, Aline Kieffer, Michel Univ Paris 11 SUPELEC CNRS L2S F-91192 Gif Sur Yvette France INRIA F-35042 Rennes France CNRS Telecom ParisTech LTCI F-75013 Paris France Inst Univ France Paris France

ISBN: (纸本)9781479903566

This paper considers the problem of lossy source coding with side information at the decoder only, for Gaussian sources, when the joint statistics of the sources are partly unknown. We propose a practical universal coding scheme based on scalar quantization and non-binary LDPC codes, which avoids the binarization of the quantized coefficients. We first explain how to choose the rate and to construct the EDPC coding matrix. Then, a decoding algorithm that jointly estimates the source sequence and the joint statistics of the sources is proposed. The proposed coding scheme suffers no loss compared to the practical coding scheme with same rate hut known variance.

关键词： Source coding with side information Rate-distortion Low Density Parity Check codes expectation maximization algorithm

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Multiple target tracking using hough transform PMHT algorithm 1

Multiple target tracking using hough transform PMHT algorith...

引用

1st International IEEE Symposium on Intelligent Systems

作者： Alexiev, KM Bulgarian Acad Sci Cent Lab Parallel Proc BU-1113 Sofia Bulgaria

ISBN: (纸本)0780376013

The problem of estimating the parameters that determine a mixture density has been subject of extraordinary interest in the last years. Such a mixture density estimation problem is multi-target tracking. One of the new data association/tracking algorithms used for multi-target tracking is Probabilistic Multi-Hypothesis Tracker proposed by Streit and Luginbuhl. This paper considers a modification of Probabilistic Multi-Hypothesis Tracker. The implementation of Hough Transform is proposed to generate a good start point to the main PMHT algorithm. The suggested modification improves estimation accuracy and correctness of PMHT algorithm and overcome its main drawback of low convergence.

关键词： multiple target tracking hough transform expectation maximization algorithm

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No Change, No Gain: Empowering Graph Neural Networks with Expected Model Change maximization for Active Learning 37

No Change, No Gain: Empowering Graph Neural Networks with Ex...

引用

37th Conference on Neural Information Processing Systems (NeurIPS)

作者： Song, Zixing Zhang, Yifei King, Irwin Chinese Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9781713899921

Graph Neural Networks (GNNs) are crucial for machine learning applications with graph-structured data, but their success depends on sufficient labeled data. We present a novel active learning (AL) method for GNNs, extending the Expected Model Change maximization (EMCM) principle to improve prediction performance on unlabeled data. By presenting a Bayesian interpretation for the node embeddings generated by GNNs under the semi-supervised setting, we efficiently compute the closed-form EMCM acquisition function as the selection criterion for AL without re-training. Our method establishes a direct connection with expected prediction error minimization, offering theoretical guarantees for AL performance. Experiments demonstrate our method's effectiveness compared to existing approaches, in terms of both accuracy and efficiency.

关键词： expectation maximization algorithm

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Texture Analysis based on Gaussian Mixture Modeling

Texture Analysis based on Gaussian Mixture Modeling

引用

World Congress on Nature and Biologically Inspired Computing

作者： Sobha, T. Remya, S. Adi Shankara Inst Engn & Technol Dept Comp Sci Kalady 683574 Kerala India Univ Kerala Dept Comp Sci Thiruvananthapuram 695121 Kerala India

ISBN: (纸本)9781424450534

Gaussian mixture modeling is a recent approach in texture analysis and is used to model image textures. Texture is modeled using a mixture of Gaussian distributions, which capture the local statistical properties of the texture. The mixture parameters are estimated using expectation maximization algorithm. This algorithm finds the maximum likelihood estimate of the parameters of an underlying distribution from a given data set when data is incomplete. The paper presents a method of identifying changes as well as new patterns in the image using the Gaussian mixture model parameters. Model parameters of the original image texture are computed. Unexpected patterns in the image are discriminated by using weighted normalized Euclidean distance measure derived from the model parameters.

关键词： Texture analysis Gaussian mixtures expectation maximization algorithm Texture modeling Texture discrimination

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Convergence and Consistency Results in Spectral Clustering and Gaussian Mixture Models

Convergence and Consistency Results in Spectral Clustering a...

引用

作者： Zhao, Ruofei University of Michigan

学位级别：博士

Clustering analysis is a popular technique in statistics and machine learning. Despite its wide use, certain theoretical properties of clustering algorithms are not well-understood. In this dissertation, we investigate two problems concerning the convergence and consistency of clustering algorithms. The first problem is on the uniform consistency of spectral embeddings in spectral clustering and in, more generally, spectral methods. The second problem is on the statistical convergence of the expectation maximization (EM) algorithm on Gaussian mixture models. Specifically, in the first problem, we study the convergence of the spectral embeddings obtained from the leading eigenvectors of certain similarity matrices to their population counterparts. We opt to study this convergence in a uniform (instead of average) sense and highlight the benefits of this choice. Using the Newton-Kantorovich Theorem and other tools from functional analysis, we first establish a general perturbation result for orthonormal bases of invariant subspaces. We then apply this general result to normalized spectral clustering. By tapping into the rich literature of Sobolev spaces and exploiting some concentration results in Hilbert spaces, we are able to prove a finite sample error bound on the uniform consistency error of the spectral embeddings in normalized spectral clustering. In the second problem, we study the convergence behavior of the EM algorithm on Gaussian mixture models with an arbitrary number of mixture components and mixing weights. We show that as long as the means of the components are separated by an amount proportional to the square root of the smaller of the number of components and the dimension, the EM algorithm converges locally to the global optimum of the log-likelihood. Further, we show that the convergence rate is linear and characterize the size of the basin of attraction to the global optimum.

关键词： Gaussian mixture models Spectral embedding Spectral clustering expectation maximization algorithm Thesis

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Em algorithm applied to non-stationary noisy phase signals

Em algorithm applied to non-stationary noisy phase signals

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Taleb, A Ericsson Radio Syst AB SE-16480 Stockholm Sweden

ISBN: (纸本)0780374029

This paper concerns the application of the EM algorithm for the estimation of the parameters of non-stationary noisy phase mono component signals buried in additive noise. Noisy phase signals are appropriate for modeling real world signals such as radar and communications signals. The maximum likelihood estimator for the signal parameters is explicited. The problem is then formulated as a missing data problem which enables a natural use of the expectation-maximization algorithm. A robust initialization scheme based on non-parametric time-frequency distributions is presented. Experimental results with both real and simulated data show the efficiency of the procedure.

关键词： optimisation maximum likelihood estimation noise time-frequency analysis statistical analysis signal processing frequency modulation EM algorithm nonstationary noisy phase signals expectation maximization algorithm parameter estimation noisy phase mono component signals additive noise radar signals communications signals maximum likelihood estimator MLE signal parameters estimation missing data problem robust initialization nonparametric time-frequency distributions quadratic FM signal amplitude modulation AM simulated data real data

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Robust object tracking algorithm in natural environments

引用

2nd International Conference on Natural Computation (ICNC 2006)

作者： Hu, Shi-qiang Liang, Guo-zhuang Jing, Zhong-liang Hebei Univ Sci & Technol Coll Informat & Elect Shijiazhuang 050054 Peoples R China Shanghai Jiao Tong Univ Inst Informat & Elect Shanghai 200030 Peoples R China

ISBN: (纸本)3540459073

In order to realize robust visual tracking in natural environments, a novel algorithm based on adaptive appearance model is proposed. The model can adapt to changes in object appearance over time. A mixture of three Gaussian distributions models the value of each pixel. An online expectation maximization (EM) algorithm is developed to update the parameters of the Gaussians. The observation model in the particle filter is designed based on the adaptive appearance model. Numerous experimental results demonstrate that our proposed algorithm can track objects well under illumination change, large pose variation, and partial or full occlusion.

关键词： visual tracking adaptive appearance model expectation maximization algorithm particle filter

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Unsupervised algorithm for radiographic image segmentation based on the Gaussian mixture model

Unsupervised algorithm for radiographic image segmentation b...

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International Conference on Computer as a Tool (EUROCON 2007)

作者： Mekhalfa, Faiza Nacereddine, Nafaa Goumeidane, Aicha Baya Welding & NDT Res Ctr Image & Signal Proc Lab Cheraga Algeria

ISBN: (纸本)9781424408122

In this paper we study an unsupervised algorithm for radiographic image segmentation, based on the Gaussian mixture models (GMMs). Gaussian mixture models constitute a well-known type of probabilistic neural networks. One of their many successful applications is in image segmentation. Mixture model parameters have been trained using the expectation maximization (EM) algorithm. Numerical experiments using radiographic images illustrate the superior performance of EM method in term of segmentation accuracy compared to fuzzy c-means algorithm.

关键词： weld defect radiographic images image segmentation Gaussian mixture model expectation maximization algorithm fuzzy c-means algorithm

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Traffic Speed Forecasting by Mixture of Experts

Traffic Speed Forecasting by Mixture of Experts

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14th International IEEE Conference on Intelligent Transportation Systems (ITSC)

作者： Coric, Vladimir Wang, Zhuang Vucetic, Slobodan Temple Univ Philadelphia PA 19122 USA Siemens Res Corp Princeton NJ 08540 USA

ISBN: (纸本)9781457721977

Traffic speed is one of the most important quantities for travel information systems. Accurate speed forecasting can help in trip planning by allowing travelers to avoid the congested routes, either by choosing the alternative routes or by changing the departure time. It is also helpful for traffic monitoring, control, and planning. An important feature of traffic is that it consists of free flow and congested regimes, which have significantly different properties. Training a single traffic speed predictor for both regimes typically results in suboptimal accuracy. To address this problem, a mixture of experts algorithm which consists of two regime-specific linear predictors and a decision tree gating function was developed. A generalized expectation maximization algorithm was used to train the linear predictors and the decision tree. The proposed algorithm was evaluated on a 5-mile stretch of I35 highway in Minneapolis containing 10 single loop detector stations, with prediction horizons ranging from 5 minutes to one hour ahead. Experimental results showed that mixture of experts approach outperforms several popular benchmark approaches.

关键词： Forecasting Linear regression Markov processes Prediction algorithms Regression tree analysis Road transportation decision tree decision tree gating function decision trees expectation maximization algorithm expectation-maximisation algorithm experts algo

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