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

共 495 条记录，以下是461-470 订阅

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Distributed tls estimation under random data faults

Distributed tls estimation under random data faults

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： S. Silva Pereira A. Pages-Zamora R. Lopez-Valcarce SPCOM Group Univ. Politec. de Catalunya-Barcelona Tech Barcelona Spain

ISBN: (纸本)9781467369985

This paper addresses the problem of distributed estimation of a parameter vector in the presence of noisy input and noisy output data, as well as data faults, performed by a wireless sensor network in which only local interactions among the nodes are allowed. In the presence of unreliable observations, standard estimators become biased and perform poorly in low signal-to-noise ratios. We propose therefore two different distributed approaches based on the expectation-Maximization algorithm: in the first one the regressors are estimated at each iteration, whereas the second one does not require explicit regressor estimation. Numerical results show that the proposed methods approach the performance of a clairvoyant scheme with knowledge of the random data faults.

关键词： Diffusion distributed estimation expectation-maximization sensor networks total least squares random data Sensor networks faults Noise Least squares method Wireless sensor networks expectation-maximisation algorithm Disease Predictive Factor Distribute signal to noise ratios diffusion

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An innovative multimodal/multispectral image registration method for medical images based on the expectation-maximization algorithm

An innovative multimodal/multispectral image registration me...

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

作者： Edgar Arce-Santana Daniel U. Campos-Delgado Aldo Mejia-Rodriguez Isnardo Reducindo Facultad de Ciencias Universidad Autonoma de San Luis Potosi SLP Mexico

ISBN: (纸本)9781424492695

In this paper, we present a methodology for multimodal/multispectral image registration of medical images. This approach is formulated by using the expectation-Maximization (EM) methodology, such that we estimate the parameters of a geometric transformation that aligns multimodal/multispectral images. In this framework, the hidden random variables are associated to the intensity relations between the studied images, which allow to compare multispectral intensity values between images of different modalities. The methodology is basically composed by an iterative two-step procedure, where at each step, a new estimation of the joint conditional multispectral intensity distribution and the geometric transformation is computed. The proposed algorithm was tested with different kinds of medical images, and the obtained results show that the proposed methodology can be used to efficiently align multimodal/multispectral medical images.

关键词： EM medical images based expectation-maximization algorithm expectation-maximisation algorithm image registration Geometric transformation images Medical imaging Medical intensity distribution

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Visual Tracking with Sensing Dynamics Compensation Using the expectation-Maximization algorithm

Visual Tracking with Sensing Dynamics Compensation Using the...

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American Control Conference

作者： Chung-Yen Lin Cong Wang Masayoshi Tomizuka Department of Mechanical Engineering University of California at Berkeley Berkeley CA 94720 USA.

ISBN: (纸本)9781479901777

Advances in vision-based technologies allow robots to perform sophisticated and intelligent tasks. Even with these advances, there still remain inherent problems with using vision-based technologies. Slow sampling rate and large latency is a problem associated with most vision hardware used in industry. We refer to these characteristics as the sensing dynamics associated with the vision sensor. This paper presents a compensation method that alleviates sensing dynamics issues in visual feedback tracking problems. We view the sensing dynamics compensation problem as two separate mathematical problems. Namely, we first deal with identifying the target model and then we deal with estimating the target position using the identified model and delayed measurements. The expectation-Maximization algorithm and Kalman filtering are utilized to solve each problem respectively. The visual servo scheme associated with the proposed approach is also studied. Simulations and experiments are designed to test the performance capability of the proposed method.

关键词： expectation-Maximization algorithm vision-based technologies perform sophisticated expectation-maximisation algorithm visual tracking compensation method vision sensors dynamics compensated Kalman filtering

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Identification of dynamical biological systems based on random effects models

Identification of dynamical biological systems based on rand...

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

作者： Levy Batista Thierry Bastogne El-Hadi Djermoune CRAN CNRS UMR 7039 BP 70239 F-54506 Vandoeuvre-les-Nancy Cedex France

ISBN: (纸本)9781424492695

System identification is a data-driven modeling approach more and more used in biology and biomedicine. In this application context, each assay is always repeated to estimate the response variability. The inference of the modeling conclusions to the whole population requires to account for the inter-individual variability within the modeling procedure. One solution consists in using random effects models but up to now no similar approach exists in the field of dynamical system identification. In this article, we propose a new solution based on an ARX (Auto Regressive model with eXternal inputs) structure using the EM (expectation-maximisation) algorithm for the estimation of the model parameters. Simulations show the relevance of this solution compared with a classical procedure of system identification repeated for each subject.

关键词： ARX EM Identification ARX gene random effect System identification Response Variability biomedicine biological organs Authentication expectation-maximisation algorithm Solutions

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EM based Joint Channel Estimation and Signal Detection for V-BLAST in MIMO OFDM Systems

EM based Joint Channel Estimation and Signal Detection for V...

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2005年无线通信、网络和移动计算国际会议

作者： Bowei Song Wenjun Zhang Lin Gui Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)078039335X

Perfect channel estimation is crucial to the detection of the V-BLAST(Vertical Bell Laboratories Layered Space-Time). The performance of V-BLAST will degrade dramatically when there exists large channel estimation error. Conventionally, the channel estimation is processed independently from the V-BLAST signal processing. In this paper, we propose an EM based algorithm to detect the transmitted V-BLAST structured signals in MIMO OFDM systems while estimate the channel impulse response (CIR) iteratively. Simulation results show that compared with conventional channel estimation methods, this algorithm is bandwidth efficient and has better performance in fast fading multi-path channels.1

关键词： MIMO systems OFDM modulation antenna arrays channel estimation expectation-maximisation algorithm fading channels multipath channels signal detection MIMO OFDM systems V-BLAST Vertical Bell Laboratories Layered Space-Time channel impul

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Variational Bounds for Mixed-Data Factor Analysis 10

Variational Bounds for Mixed-Data Factor Analysis

引用

Annual Conference on Neural Information Processing Systems

作者： Mohammad Emtiyaz Khan Benjamin M. Marlin Guillaume Bouchard Kevin P. Murphy University of British Columbia Vancouver BC Canada V6T 1Z4 Xerox Research Center Europe 38240 Meylan France

ISBN: (纸本)9781617823800

We propose a new variational EM algorithm for fitting factor analysis models with mixed continuous and categorical observations. The algorithm is based on a simple quadratic bound to the log-sum-exp function. In the special case of fully observed binary data, the bound we propose is significantly faster than previous variational methods. We show that EM is significantly more robust in the presence of missing data compared to treating the latent factors as parameters, which is the approach used by exponential family PCA and other related matrix-factorization methods. A further benefit of the variational approach is that it can easily be extended to the case of mixtures of factor analyzers, as we show. We present results on synthetic and real data sets demonstrating several desirable properties of our proposed method.

关键词： Factor analysis Bound expectation-maximisation algorithm missing data Dataset SYNTHETICS mixtures Calculus of variation binary data Analysers

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A UNIVERSAL IMAGE CODING APPROACH USING SPARSE STEERED MIXTURE-OF-EXPERTS REGRESSION

A UNIVERSAL IMAGE CODING APPROACH USING SPARSE STEERED MIXTU...

引用

IEEE International Conference on Image Processing

作者： Ruben Verhack Thomas Sikora Lieven Lange Glenn Van Wallendael Peter Lambert Ghent University - iMinds - Data Science Lab Technische Universitat Berlin - Communication Systems Lab

ISBN: (纸本)9781467399623

Our challenge is the design of a "universal" bit-efficient image compression approach. The prime goal is to allow reconstruction of images with high quality. In addition, we attempt to design the coder and decoder "universal", such that MPEG-7-like low-and mid-level descriptors are an integral part of the coded representation. To this end, we introduce a sparse Mixture-of-Experts regression approach for coding images in the pixel domain. The underlying stochastic process of the pixel amplitudes are modelled as a 3-dimensional and multi-modal Mixture-of-Gaussians with K modes. This closed form continuous analytical model is estimated using the expectation-Maximization algorithm and describes segments of pixels by local 3-D Gaussian steering kernels with global support. As such, each component in the mixture of experts steers along the direction of highest correlation. The conditional density then serves as the regression function. Experiments show that a considerable compression gain is achievable compared to JPEG for low bitrates for a large class of images, while forming attractive low-level descriptors for the image, such as the local segmentation boundaries, direction of intensity flow and the distribution of these parameters over the image.

关键词： Mixture of Experts Gaussian Mixture Regression Steering Regression Gaussian Mixture Models Sparse Image Coding images pixel Mixture models image reconstruction expectation-maximisation algorithm Imagery (Psychotherapy) JPEG Data compression Image compression Gauss Descriptors Subject Headings

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Cluster analysis of vehicle driving conditions based on K-means algorithm and EM algorithm

Cluster analysis of vehicle driving conditions based on K-me...

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CSAA/IET International Conference on Aircraft Utility Systems (AUS 2020)

作者： Q. Yan Y. Ma Y. Li School of Mechanical Engineering Beijing Institute of Technology Beijing China

The simulation and evaluation of land vehicle performance requires accurate representation of driving conditions. The ability to effectively interpret this condition to estimate the power demand is important for the energy management and plays crucial role in the battery utilization. After collecting driving status of vehicle, two algorithms (K-means Clustering and EM algorithm) are employed for clustering the working condition blocks. Finally, compare the clustering effects of the two algorithms, and draw the conclusion that EM algorithm is better.

关键词： vehicle dynamics energy management systems pattern clustering expectation-maximisation algorithm automobiles kinematics mechanical engineering computing

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An expectation-Maximization Method for Calibrating Synchronous Machine Models

An Expectation-Maximization Method for Calibrating Synchrono...

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IEEE Power and Energy Society General Meeting

作者： Da Meng Ning Zhou Shuai Lu Guang Lin PNNL Richland WA

ISBN: (纸本)9781479913022

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using the maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using the EM method also are presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

关键词： Extended Kalman Filter EM algorithms State Estimation Parameter Calibration State estimation Extended Kalman filters synchronous machines expectation-maximisation algorithm single machine infinite bus system Phasor measurement units dynamic state Noise measurement maximum likelihood estimation parameter uncertainty machine model

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Variational Inference for Crowdsourcing 12

Variational Inference for Crowdsourcing

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Annual Conference on Neural Information Processing Systems

作者： Qiang Liu Jian Peng Alexander Ihler ICS UC Irvine TTI-C & CSAIL MIT

ISBN: (纸本)9781627480031

Crowdsourcing has become a popular paradigm for labeling large datasets. However, it has given rise to the computational task of aggregating the crowdsourced labels provided by a collection of unreliable annotators. We approach this problem by transforming it into a standard inference problem in graphical models, and applying approximate variational methods, including belief propagation (BP) and mean field (MF). We show that our BP algorithm generalizes both majority voting and a recent algorithm by Karger et al. [1], while our MF method is closely related to a commonly used EM algorithm. In both cases, we find that the performance of the algorithms critically depends on the choice of a prior distribution on the workers' reliability; by choosing the prior properly, both BP and MF (and EM) perform surprisingly well on both simulated and real-world datasets, competitive with state-of-the-art algorithms based on more complicated modeling assumptions.

关键词： Variational Inference Crowdsource Prior distribution algorithms expectation-maximisation algorithm Dataset Belief propagation Graphical models Calculus of variation

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