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检索条件"主题词=expectation-maximization algorithms"

共 270 条记录，以下是201-210 订阅

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Iterative Synchronization: EM Algorithm Versus Newton-Raphson Method

Iterative Synchronization: EM Algorithm Versus Newton-Raphso...

引用

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

作者： C. Herzet X. Wautelet V. Ramon L. Vandendorpe Communications Laboratory Université catholique de Louvain Louvain-la-Neuve Belgium

This paper deals with iterative maximum-likelihood synchronization of a scalar parameter. An efficient implementation of the Newton-Raphson (NR) maximum-search method is proposed. Considering the latter implementation, the NR approach is shown to be an attractive alternative to synchronization methods based on the expectation-maximization (EM) algorithm. Simulation results for the case of phase-offset synchronization show that NR method usually increases the speed of convergence of the synchronization algorithm

关键词： Iterative methods Iterative algorithms Newton method Maximum likelihood estimation Convergence Parameter estimation Laboratories Signal to noise ratio Sum product algorithm expectation-maximization algorithms

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Multiscale Reconstruction for Photon-Limited Shifted Excitation Raman Spectroscopy

Multiscale Reconstruction for Photon-Limited Shifted Excitat...

引用

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

作者： Rebecca Willett Department of Electrical and Computer Engineering Duke University Durham NC USA

Shifted excitation Raman spectroscopy results in multiple observations of the sum of a material's fluorescent and Raman spectra. The fluorescent spectrum is typically stationary with respect to the excitation frequency induced by the instrument, while the Raman spectrum is subject to a nonlinear shift which depends explicitly and in a known manner upon the excitation frequency. This phenomenon has been exploited to reconstruct Raman spectra indirectly by subtracting spectra observed at two closely spaced excitation frequencies. The technique, known as shifted excitation Raman difference spectroscopy (SERDS), is of limited utility, however, in that observations with low photon counts are difficult to process accurately, and that one must still reconstruct the spectrum from the estimate of the derivative. This paper presents an innovative alternative approach to Raman spectrum reconstruction based on an expectation-maximization algorithm and multiresolution photon-limited signal analysis. Using this method, it is shown that using multiple excitation frequencies (while keeping the total excitation laser power and total expected photon counts constant) can result in dramatic improvements in reconstruction accuracy.

关键词： Raman scattering Spectroscopy Frequency Fluorescence Laser excitation Instruments expectation-maximization algorithms Signal resolution Signal analysis Power lasers

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Novel EM-based Method for Channel Estimation and Signal Detection in MIMO-OFDM Systems

Novel EM-based Method for Channel Estimation and Signal Dete...

引用

Eurocon

作者： D. Obradovic A. Szabo D. Mandic Rui Chang Technology division Siemens Corporate Munich Germany Electrical Engineering Department Imperial College London London UK

This paper presents a novel method for channel estimation and signal detection in MIMO-OFDM systems based on the expectation-maximization algorithm (EM). In contrast to standard pilot aided interpolation methods, the estimation of the channel in a given frequency bin without pilot symbols is posed as a maximum likelihood (ML) with missing observations and is solved by the iterative EM algorithm. As a semi-blind estimation method, EM is insensitive to permutation and scaling of transmitted signals. To avoid this uncertainty, the initialization of the iterative optimization for each frequency bin is achieved using the model of the neighboring bin, provided that these bins satisfy thus introduced similarity condition. Hence, channel estimation is performed sequentially over all bins starting from the bin for which its model is estimated with a pilot sequence. The proposed method is tested successfully on MIMO-OFDM systems in realistic channel conditions

关键词： Channel estimation Signal detection Frequency estimation Maximum likelihood estimation expectation-maximization algorithms Interpolation Maximum likelihood detection Iterative methods Iterative algorithms Uncertainty

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Evolving polydistributional mixtures for mammographic feature modeling and analysis

Evolving polydistributional mixtures for mammographic featur...

引用

IEEE International Conference on Evolutionary Computation

作者： D.E. Waagen M.D. Parsons J.R. McDonnell F.L. Datz TRW Systems Integration Group Ogden UT USA RDT&E Div NCCOSC San Diego CA USA University of Utah Health Sciences Center Salt Lake UT USA

This work investigates the application of stochastic optimization in the development of a new paradigm, polydistributional mixtures, for automated data modeling. This new paradigm increases the generality of mixture methods by allowing for the automated simultaneous optimization of the number of components, the distributional form of each component, the proportionality associated with each component, as well as the parameters of each component. A general evolutionary approach, based on the mutation and selection from a population of potential solutions, is used as the optimization procedure. The approach is applied to modeling texture features extracted from digitized mammograms of cancerous and noncancerous regions. The resulting polydistributional mixtures are compared to optimal (in terms of model fitness) normal mixtures optimized via the expectation-maximization algorithm.

关键词： Stochastic processes Probability distribution Cost function Cities and towns Stochastic systems Optimization methods Genetic mutations Feature extraction expectation-maximization algorithms Probability density function

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Covariance estimation for multidimensional data using the EM algorithm

Covariance estimation for multidimensional data using the EM...

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Asilomar Conference on Signals, Systems & Computers

作者： T.A. Barton D.R. Fuhrmann Department of Electrical Engineering Washington University Saint Louis MO USA

Under a complex-Gaussian data model, a maximum likelihood method based on the iterative expectation-maximization algorithm is given to estimate structured covariance matrices for multidimensional data organized into column-vector form. The covariance structures of interest involve a hierarchy of subblocks within the covariance matrix, and include block-circulant and block Toeplitz matrices and their generalizations. These covariance matrices are elements of certain covariance constraint sets such that each element may be described as a matrix multiplication of a known matrix of Kronecker products and a nonnegative-definite, block-diagonal matrix. Several convergence properties of the estimation procedure are discussed, and an example of algorithm behavior is provided.< >

关键词： Multidimensional systems Covariance matrix Data models Maximum likelihood estimation Iterative methods expectation-maximization algorithms Convergence

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Photon-limited image denoising by inference on multiscale models

Photon-limited image denoising by inference on multiscale mo...

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IEEE International Conference on Image Processing

作者： Stamatios Lefkimmiatis George Papandreou Petros Maragos School of ECE National and Technical University of Athens Athens Greece

We present an improved statistical model of Poisson processes, with applications to photon-limited imaging. We build on previous work, adopting a multiscale representation of the Poisson process in which the ratios of the underlying Poisson intensities (rates) in adjacent scales are modeled as mixtures of conjugate parametric distributions. Our main novel contributions are (1) a rigorous and robust regularized expectation-maximization (EM) algorithm for maximum-likelihood estimation of the rate-ratio density parameters directly from the observed Poisson data (counts); (2) extension of the method to work under a scale-recursive hidden Markov tree model (HMT) which couples the mixture label assignments in consecutive scales, thus modeling inter-scale coefficient dependencies in the vicinity of edges; and (3) exploration of a fully 2-D quad-tree image partitioning, involving Dirichlet-mixture rate-ratio densities, instead of the conventional separable binary image partitioning involving Beta-mixture rate-ratio densities. Experimental intensity estimation results on standard images with artificially simulated Poisson noise and photon-limited images with real shot noise demonstrate the effectiveness of the proposed approach.

关键词： Image denoising Hidden Markov models Parameter estimation Bayesian methods Robustness Gunshot detection systems expectation-maximization algorithms Degradation Fluctuations Pixel

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The EM algorithm in independent component analysis

The EM algorithm in independent component analysis

引用

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

作者： K.B. Petersen O. Winther ISP IMM Technical University of Denmark Kongens Lyngby Denmark

We investigate two techniques for independent component analysis which use the expectation-maximization algorithm. Analysis and simulations show that convergence becomes extraordinarily slow for almost all cases, compared to other optimization techniques. The two alternatives considered are "adaptive overrelaxed EM" and Ucminf (a BFGS with soft line search), which both improves the convergence dramatically with little or no extra analytical work. We discuss the generality and perspectives of the findings.

关键词： Independent component analysis Convergence Covariance matrix Acceleration Algorithm design and analysis Cost function expectation-maximization algorithms Analytical models Maximum likelihood estimation Proposals

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Actuator networks: distributed evaluation of causal effect

Actuator networks: distributed evaluation of causal effect

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Asilomar Conference on Signals, Systems & Computers

作者： M. Coates G. Ing Department of Electrical and Computer Engineering McGill University Montreal QUE Canada

We study sensor-actuator networks, extensions of sensor networks that consist of nodes that both monitor and interact with the environment. In particular, we focus on the evaluation of average causal effect within such networks. We describe a distributed algorithm that enables individual actuator nodes to determine the probable consequences of local action on the global environment and hence decide if such action is conducive to achieving the aims of the network. Our approach represents the relationship between actuation and sensor measurements using a causal graph, and applies a distributed expectation-maximization algorithm to estimate the average causal effect of actuation. We evaluate the effectiveness of our approach through simulations that examine the benefits of including side-information regarding possible event outcomes.

关键词： Actuators Wireless sensor networks Distributed algorithms Graphical models Probability distribution Computerized monitoring expectation-maximization algorithms Discrete event simulation Performance evaluation Biomedical monitoring

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Unsupervised Image Layout Extraction

Unsupervised Image Layout Extraction

引用

IEEE International Conference on Image Processing

作者： David Liu Datong Chen Tsuhan Chen Department of Electrical and Computer Engineering School of Computer Science Carnegie Mellon University Pittsburgh USA

We propose a novel unsupervised learning algorithm to extract the layout of an image by learning latent object-related aspects. Unlike traditional image segmentation algorithms that segment an image using feature similarity, our method is able to learn high-level object characteristics (aspects) from a large number of unlabeled images containing similar objects to facilitate image segmentation. Our method does not require human to annotate the training set and works without supervision. We use a graphical model to address the learning of aspects and layout extraction together. In particular, aspect-feature dependency from multiple images is learned via the expectation-maximization algorithm. We demonstrate that, by associating latent aspects to spatial structure, the proposed method achieves much better layout extraction results than using probabilistic latent semantic analysis.

关键词： Image segmentation Unsupervised learning Data mining Graphical models Layout Image analysis Object detection Computer science Humans expectation-maximization algorithms

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Linear Dynamical System Response Modeling of Trial-to-Trial Amplitude Variability in Event-Related MEG/EEG

Linear Dynamical System Response Modeling of Trial-to-Trial ...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Tulaya Limpiti Barry D. Van Veen Srikantan S. Nagarajan Hagai T. Attias Department of Electrical and Computer Engineering University of Wisconsin Madison Madison WI USA Biomagnetic Imaging Lab Department of Radiology University of California San Francisco San Francisco CA USA Joint Graduate Group in Bioengineering UCSF UC Berkeley CA USA Golden Metallic Inc. San Francisco CA USA

We propose a linear dynamical system response (LDSR) model to describe amplitude variability across trials in event-related magnetoencephalographic/electroencephalographic (MEG/EEG) data. Variability across trials may reflect habituation, fatigue, or changes in cognitive states of the brain. A wide range of trial-to-trial variability can be represented using the LDSR model, including the constant response (CR) model as a limiting case. The spatiotemporal signal waveform is assumed constant but unknown, and is represented using a linear combination of spatial and temporal basis functions. The background noise is assumed to be spatially correlated with unknown covariance matrix. We obtain the maximum-likelihood estimates of the amplitude variability and signal waveform via a generalized expectation-maximization algorithm. The expectation step involves a Kalman fixed-interval smoother which tracks the trial-to-trial amplitude variability while the maximization step estimates the signal waveform, spatial noise covariance, and LDSR model parameters. We demonstrate the effectiveness of the proposed model using both real and simulated evoked response data. The performance of the algorithm is analyzed in terms of the mean squared error of the amplitude estimates.

关键词： Brain modeling Electroencephalography Amplitude estimation Fatigue Chromium Spatiotemporal phenomena Background noise Covariance matrix Maximum likelihood estimation expectation-maximization algorithms

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