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

共 448 条记录，以下是431-440 订阅

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SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

SCALABLE BAYESIAN NONPARAMETRIC DICTIONARY LEARNING

引用

European Signal Processing Conference

作者： Sarper Sertoglu John Paisley Department of Computer Science Columbia University Department of Electrical Engineering Columbia University

ISBN: (纸本)9781479988518

We derive a stochastic EM algorithm for scalable dictionary learning with the beta-Bernoulli process, a Bayesian nonparametric prior that learns the dictionary size in addition to the sparse coding of each signal. The core EM algorithm provides a new way for doing inference in nonparametric dictionary learning models and has a close similarity to other sparse coding methods such as K-SVD. Our stochastic extension for handling large data sets is closely related to stochastic variational inference, with the stochastic update for one parameter exactly that found using SVI. We show our algorithm compares well with K-SVD and total variation minimization on a denoising problem using several images.

关键词： expectation-maximisation algorithm Sparse coding Dictionaries as Topic one-parameter Stochastic Sludge volume index Learning Dataset Variational Inference

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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

作者： Elsa DUPRAZ Aline ROUMY Michel KIEFFER L2S - CNRS - SUPELEC - Univ Paris-Sud 91192 Gif-sur-Yvette France INRIA Campus de Beaulieu 35042 Rennes France

ISBN: (纸本)9781479903573

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 LDPC 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 but known variance.

关键词： Source coding with side information Rate-distortion Low Density Parity Check codes expectation Maximization algorithm low density parity check codes expectation-maximisation algorithm Rate distortion labeling scheme Gauss scalar quantization lossy source coding binarization

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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...

引用

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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Fast Bayesian Signal Recovery in Compressed Sensing with Partially Unknown Discrete Prior

Fast Bayesian Signal Recovery in Compressed Sensing with Par...

引用

WSA 2017;21th International ITG Workshop on Smart Antennas

作者： Norbert Goertz Gabor Hannak

Bayesian Approximate Message Passing (BAMP) provides excellent recovery performance in Compressed Sensing (CS), but one seemingly needs to know the pdf of the signal prior. If the shape of the pdf is known but not its parameters, we show how they can be estimated with very low complexity during the BAMP iterations by the well-known Method of Moments (MoM). We compare the new approach with an established scheme from the literature that is based on the expectation Maximization (EM) algorithm. By simulations we show that the MoM-based BAMP scheme works at least as good as the EM-based approach and with much lower complexity.

关键词： Compressed sensing Signal restoration METHOD OF MOMENTS Bayesian inference expectation-maximisation algorithm

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AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SENSING MULTITEMPORAL IMAGES

AUTOMATIC DETECTION OF BURNED AREAS IN WETLANDS BY REMOTE SE...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Daniel Capella Zanotta Hiran Zani Yosio Edemir Shimabukuro National Institute for Space Research - S?o José dos Campos - SP - Brazil

ISBN: (纸本)9781479911127

In this paper, a methodology for automatic detection of burned areas is suggested. The classification criterion is performed using Bayesian statistical parameter (mean and covariance matrix) extracted automatically using the expectation Maximization algorithm and taking into account the spectral similarity between burned and flooded areas. In this work the final process involves the application of morphological operators of erosion and dilation of images in order to insert information from the spatial context, refining the final map. Experiments were conducted to a TM-Landsat scene with areas affected by fires and seasonal flooding. The results show that the accuracy is increased with the consideration of flooding mask and the subsequent application of spatial context, reaching values up to 97% of accuracy when compared with a reference map.

关键词： Optical imagery Land surface change Pattern recognition Optical Imaging Pattern recognition Wetlands automatic detection expectation-maximisation algorithm AUTOMATIC REFERENCE MAPS images morphological operator Flooded areas remote sensing Burns spatial context variance covariance matrix

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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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Probabilistic Principal Geodesic Analysis 13

Probabilistic Principal Geodesic Analysis

引用

Annual Conference on Neural Information Processing Systems

作者： Miaomiao Zhang P. Thomas Fletcher School of Computing University of Utah Salt Lake City UT

ISBN: (纸本)9781632660244

Principal geodesic analysis (PGA) is a generalization of principal component analysis (PCA) for dimensionality reduction of data on a Riemannian manifold. Currently PGA is defined as a geometric fit to the data, rather than as a probabilistic model. Inspired by probabilistic PCA, we present a latent variable model for PGA that provides a probabilistic framework for factor analysis on manifolds. To compute maximum likelihood estimates of the parameters in our model, we develop a Monte Carlo expectation Maximization algorithm, where the expectation is approximated by Hamiltonian Monte Carlo sampling of the latent variables. We demonstrate the ability of our method to recover the ground truth parameters in simulated sphere data, as well as its effectiveness in analyzing shape variability of a corpus callosum data set from human brain images.

关键词： principal components analysis Prostaglandins A Probability Probabilistic Model dimensionality reduction Manifolds Factor analysis Passive Cutaneous Anaphylaxis Brain Imaging Hypoderma Generalization expectation-maximisation algorithm

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Design of a Cosmic-ray Muon Tomography System for Dry Storage Cask Imaging

Design of a Cosmic-ray Muon Tomography System for Dry Storag...

引用

IEEE Nuclear Science Symposium

作者： Can Liao Haori Yang Department of Nuclear Engineering and Radiation Health Physics Oregon State University

ISBN: (纸本)9781479960989

The overarching goal of this project is to develop an imaging system to monitor the content of a dry storage cask (DSC) with cosmic ray muons. The imaging technique utilizing multiple Coulomb scattering was demonstrated to be a good non-destructive assay method for high-Z materials, such as used nuclear fuel assemblies in DSCs. In this paper, we report our simulation work on the 2D and 3D muon tomography of a DSC. The Point of Closest Approach (POCA) and Maximum-Likelihood expectation-Maximization (ML-EM) algorithms are used for image reconstruction. The reconstructed image showed good spatial resolution and good material discrimination ability, even though the detector has only 1cm by 1cm spatial resolution. The prototype design uses EJ-200 plastic scintillator as the detector for muon detection. Saint-Gobain BCF-92 wavelength shifting (WLS) optical fiber is used for the light transfer providing multi-channel position information. The optical fibers are coupled to two Hamamatsu H8500C MAPMTs. The PMT signals could then be measured with multi-channel QDCs. SiPM arrays are also being evaluated for readout. This prototype system is currently under construction. Preliminary tests results are shown in this paper. Full experimental results will be presented and discussed in the future.

关键词： FUEL ASSEMBLIES Imaging cosmic rays Casks detector COULOMB SCATTERING Differential scanning calorimetry MUONS Imaging systems Spatial resolution image reconstruction Optical Fibers expectation-maximisation algorithm

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Learning dictionary for efficient signal compression

Learning dictionary for efficient signal compression

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Afshin Abdi Ali Payani Faramarz Fekri Georgia Institute of Technology United States of America

ISBN: (纸本)9781509041183

We consider the problem of learning dictionaries for data compression. Different from ordinary learning methods, the objective is to design a dictionary such that the signal has a low entropy representation in the basis of the dictionary, rather than giving a sparse or low-energy representation. To achieve this goal, we need to consider the effect of quantization on the rate-distortion curve as well as an estimation of the distributions of the coefficients. Based on this probability estimation, the coefficients are computed, quantized and then entropy-coded. As such, we have developed algorithms for different classes of dictionaries;orthonormal, union of orthonormals and general dictionaries with unit-norm atoms, to iteratively learn the dictionary and the distribution models of the coefficients. A mixture of Gaussians is adopted to estimate the probability and is updated using the expectation maximization algorithm together with the dictionary learning. Simulation results on the real seismic data show the effectiveness of the proposed algorithm compared to ordinary dictionary learning methods.

关键词： Dictionary Learning Signal Compression Low-Entropy Signal Representation Dictionaries as Topic signal compression expectation-maximisation algorithm Gauss Data compression

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Study and Implementation of an Iterative Decoder for SFBC-OFDM Systems with Imperfect Channel Knowledge

Study and Implementation of an Iterative Decoder for SFBC-OF...

引用

IEEE Mediterranean Electrotechnical Conference

作者： Ayman MASSAOUDI Noura SELLAMI Nouri MASMOUDI Mohamed SIALA SUP'COM Cite Technologique des Communications ENIS Route de Soukra

ISBN: (纸本)9781424457939

In this paper, we consider the problem of the Space Frequency Block Codes (SFBC) decoding, according to the Maximum A Posteriori (MAP) criterion when the channel is not perfectly known at the receiver. We propose to use the expectation Maximization (EM) algorithm to solve this problem. Simulation results show that the performance of our iterative decoder using the EM algorithm is very close to the performance obtained when the channel is perfectly known. We also propose an efficient implementation of the iterative decoder (based on the EM algorithm) on FPGA.

关键词： Field programmable gate arrays expectation-maximisation algorithm Iterative spatial frequency Imperfect decoders space-frequency block coding

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