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

共 493 条记录，以下是411-420 订阅

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A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences 02

A Hierarchical Bayesian Markovian Model for Motifs in Biopol...

引用

Annual Conference on Neural Information Processing Systems

作者： Eric P. Xing Michael I. Jordan Richard M. Karp Stuart Russell Computer Science Division University of California Berkeley Berkeley CA 94720

ISBN: (纸本)0262025507

We propose a dynamic Bayesian model for motifs in biopolymer sequences which captures rich biological prior knowledge and positional dependencies in motif structure in a principled way. Our model posits that the position-specific multinomial parameters for monomer distribution are distributed as a latent Dirichlet-mixture random variable, and the position-specific Dirichlet component is determined by a hidden Markov process. Model parameters can be fit on training motifs using a vari-ational EM algorithm within an empirical Bayesian framework. Varia-tional inference is also used for detecting hidden motifs. Our model improves over previous models that ignore biological priors and positional dependence. It has much higher sensitivity to motifs during detection and a notable ability to distinguish genuine motifs from false recurring patterns.

关键词： Biopolymers Bayesian inference expectation-maximisation algorithm HIGH SENSITIVITY Hierarchical

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Strategies to Improve Direct EM Patlak Reconstructions

Strategies to Improve Direct EM Patlak Reconstructions

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IEEE Nuclear Science Symposium

作者： Jean-Dominique Gallezot Yihuan Lu Kathryn Fontaine Chi Liu Richard E. Carson Department of Radiology and Biomedical Imaging Yale University

ISBN: (纸本)9781538622834

Direct expectation-Maximization (EM) reconstruction of parametric images from PET data has been proposed, to take advantage of the known Poisson character of raw PET data. However, direct reconstruction algorithms are then dependent on the properties of the kinetic model and of the radiotracer used. Moreover, the first direct EM reconstruction algorithm proposed, the Parametric Iterative Reconstruction (PIR) algorithm, based on the Patlak plot, converges slowly when applied to [~(18)F]FDG data. Thus, alternative algorithms have been proposed to accelerate convergence. In this study, we propose additional strategies to improve the PIR algorithm. One strategy is to use a line search procedure to increase the size of steps taken at each iteration (PIR-LS). Another group of strategies is to apply physiological constraints (PIR-PC) on the intercept parameter of the Patlak plot, since this parameter has a small dynamic range and is more stable across subjects and across physiological and disease states. The third strategy is to apply a linear transform to the independent variables of the Patlak model (PIR-LTIV) to obtain new independent variables more favorable for convergence. PIR-LS, PIR-PC and PIR-LTIV were first compared to PIR and the Nested EM algorithm on simulated 2D data. Results showed that all new methods could increase the convergence speed of PIR, with PIR-LTIV having the fastest convergence among the new methods, and similar to that of the Nested EM algorithm. Then, PIR and PIR-LS were compared using a simulated dynamic brain dataset, PIR and PIR-PC were compared on a real lung tumor data set, and PIR-LTIV was tested on simulated brain data, real brain data, and the real lung tumor dataset. These data confirmed that all new methods could increase PIR convergence, with PIR-LTIV being the most promising since convergence speed was visually similar to that of indirect reconstructions, with lower image noise.

关键词： PIR gene new methods expectation-maximisation algorithm Converge image noise Lung Neoplasms line retrieval Reconstruction algorithm Reconstructive Surgical Procedures Independent Variables Dynamic range

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A Scenario-based Approach to Parameter Estimation in State-Space Models having Quantized Output Data

A Scenario-based Approach to Parameter Estimation in State-S...

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2010 49th IEEE Conference on Decision and Control

作者： Damian E. Marelli Boris I. Godoy Graham C. Goodwin ARC Centre of Excellence for Complex Dynamic Systems and Control The University of Newcastle Australia

ISBN: (纸本)9781424477456

In this paper we describe an algorithm for estimating the parameters of a linear, discrete-time system, in state-space form, having quantized measurements. The estimation is carried out using the maximum likelihood criterion. The solution is found using the expectation maximization (EM) algorithm. A technical difficulty in applying this algorithm for this problem is that the a posteriori probability density function, found in the EM algorithm, is non-Gaussian. To deal with this issue, we sequentially approximate it using scenarios, i.e., a weighted sum of impulses which are deterministically computed. Numerical experiments show that the proposed approach leads to a significantly more accurate estimation than the one obtained by ignoring the presence of the quantizer and applying standard estimation methods.

关键词： expectation-maximisation algorithm Parameter estimation Probability density function quantizer weight sums space configuration a posteriori probability

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algorithmic modelling of structural connectivity for process plants

Algorithmic modelling of structural connectivity for process...

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American Control Conference

作者： Temitayo Bankole Debangsu Bhattacharyya Department of Chemical Engineering West Virginia University Morgantown 26506 USA

ISBN: (纸本)9781467386838

In this paper, we propose a new systematic methodology to identify dynamic changes in the connectivity among various equipment items in a process plant. Drawing analogy to neurological systems, the paper develops a framework to obtain dynamic causal model to capture the intrinsic and stimulus-driven connectivity among equipment items. An expectation maximization algorithm following a Bayesian framework is employed to obtain the elements of the connectivity matrices using a maximum likelihood approach. The algorithm is tested on a reactor-separator system. The proposed approach can help to decompose large-scale process plants into strongly-connected and weakly-connected systems. If only strongly-connected equipment items are considered together for control structure selection, this approach can result in a computationally less intensive problem for dynamic controlled variable selection of large-scale process plants.

关键词： Mathematical model Kernel Process control Bayes methods Jacobian matrices Covariance matrices Heuristic algorithms Jacobian matrix Heuristic algorithms Bayes methods Process plants Process control Mathematical Model variance covariance matrix Kernel Covariance matrix expectation-maximisation algorithm maximum likelihood

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NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

NOISE ROBUST SPEECH DEREVERBERATION WITH KALMAN SMOOTHER

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Masahito Togami Yohei Kawaguchi Central Research Laboratory Hitachi Ltd. 1-280 Higashi-koigakubo Kokubunji-shi Tokyo 185-8601 Japan

ISBN: (纸本)9781479903573

A speech dereverberation method is proposed that is robust against background noise. In contrast to conventional methods based on the linear prediction of the given microphone input signal, in which the linear prediction coefficients are not fully optimized when there is background noise, the proposed method optimizes the coefficients by linear prediction of the noiseless reverberant speech signal even when there is background noise. The noiseless reverberant speech signal and the parameters are iteratively updated on the basis of the expectation maximization algorithm. In the expectation step, sufficient statistics of latent variables which include noiseless reverberant speech signal are estimated using the Kalman smoother. Unlike the standard Kalman smoother, which uses a time-invariant covariance matrix as a state-transition covariance matrix, the proposed method utilizes a time-varying covariance matrix, enabling it to meet the time-varying speech characteristics. The parameters are updated so that the Q function is increased in the maximization step. Experimental results show that the proposed method is superior to conventional methods under noisy conditions.

关键词： Voice Signal linear prediction BACKGROUND NOISE Q FUNCTIONS expectation-maximisation algorithm Noise variance covariance matrix Speech Characteristics Speech Covariance matrix

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Regularized EM algorithms: A Unified Framework and Statistical Guarantees 15

Regularized EM Algorithms: A Unified Framework and Statistic...

引用

Annual Conference on Neural Information Processing Systems

作者： Xinyang Yi Constantine Caramanis Dept. of Electrical and Computer Engineering The University of Texas at Austin

ISBN: (纸本)9781510825024

Latent models are a fundamental modeling tool in machine learning applications, but they present significant computational and analytical challenges. The popular EM algorithm and its variants, is a much used algorithmic tool; yet our rigorous understanding of its performance is highly incomplete. Recently, work in has demonstrated that for an important class of problems, EM exhibits linear local convergence. In the high-dimensional setting, however, the M-step may not be well defined. We address precisely this setting through a unified treatment using regularization. While regularization for high-dimensional problems is by now well understood, the iterative EM algorithm requires a careful balancing of making progress towards the solution while identifying the right structure (e.g., sparsity or low-rank). In particular, regularizing the M-step using the state-of-the-art high-dimensional prescriptions (e.g., a la) is not guaranteed to provide this balance. Our algorithm and analysis are linked in a way that reveals the balance between optimization and statistical errors. We specialize our general framework to sparse gaussian mixture models, high-dimensional mixed regression, and regression with missing variables, obtaining statistical guarantees for each of these examples.

关键词： expectation-maximisation algorithm Frameworks algorithms Regression (Psychology) variants

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Visual Tracking Based on 3D Probabilistic Reconstruction

Visual Tracking Based on 3D Probabilistic Reconstruction

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Annual IEEE International Conference on Engineering of Computer Based Systems

作者： Gisele Simas Rodrigo de Bern Lucas Novelo Guilherme Fickel Silvia Botelho Centro de Ciencias Computacionais - C3 Universidade Federal do Rio Grande - FURG

ISBN: (纸本)9781424465378;9780769540054

This paper presents an approach to the 3D visual tracking problem into multi-camera environments. This proposal executes the markerless visual tracking observing the environment through a model based in a volumetric reconstruction technique, called 3D Probabilistic Occupancy Grids, which is still seldom used for this purpose. The target is tracked by the use of expectation-Maximization algorithm with an object representation model constructed with Gaussians blobs representing the object body parts.

关键词： visual tracking expectation-maximisation algorithm Environments Reconstructive Surgical Procedures

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A Signal Processing Technique for Compensating Random Drift of MEMS Gyros

A Signal Processing Technique for Compensating Random Drift ...

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2014中国制导、导航与控制学术会议

作者： Jieyu Liu Qiang Shen Weiwei Qin Department of Automatic Control Xi''an Research Institute of High-techXi''anShannxi Province710025China

ISBN: (纸本)9781479946983

In this paper,we present a prediction and compensation method for Micro-Electro-Mechanical System (MEMS) gyroscope random drift,which is based on relevance vector *** relevance vector machine (RVM) model is established based on the feature of MEMS gyroscope random drift and the parameters are trained by the expectation Maximization (EM) *** phase space reconstruction,the time sequence of random drift is accessed in the *** final experimental results indicate that our proposed methodology can achieve both the least complexity of structure and goodness of fit to data,and also can predict the gyroscope random drift ***,by compensating random drift using the predicting result,the precision of gyroscopes application could be improved well.

关键词： compensation expectation-maximisation algorithm gyroscopes learning (artificial intelligence) microsensors signal processing EM algorithm MEMS gyroscope RVM model compensation method expectation maximization algorithm microelectromechanical systems phase space reconstruction prediction method random drift compensation relevance vector machine signal processing technique Bayes methods Gyroscopes Kernel Micromechanical devices Prediction algorithms Support vector machines Vectors

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Relevance Topic Model for Unstructured Social Group Activity Recognition 13

Relevance Topic Model for Unstructured Social Group Activity...

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Annual Conference on Neural Information Processing Systems

作者： Fang Zhao Yongzhen Huang Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781632660244

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a "relevance topic model" for jointly learning meaningful mid-level representations upon bag-of-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i.e., Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos.

关键词： social group Web video Bayesian learning video data expectation-maximisation algorithm Classifiers linear unit Training Videos state of the art Themes Variational Inference

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FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization

FilterReg: Robust and Efficient Probabilistic Point-Set Regi...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Wei Gao Russ Tedrake Massachusetts Institute of Technology

ISBN: (纸本)9781728132945

Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Point-set registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs). The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks. The video demo and source code are available on our project page.

关键词： ARTICULATED Set of points Registration expectation-maximisation algorithm maximum likelihood estimation Probability Source code Gaussian filter Twisting

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