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

共 495 条记录，以下是371-380 订阅

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Parametric Density Estimation Using EM algorithm for Collaborative Spectrum Sensing

Parametric Density Estimation Using EM Algorithm for Collabo...

引用

3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008)

作者： Shun-Te Tseng Han-Ting Chiang James S. Lehnert School of Electrical and Computer Engineering Purdue University Calumet West Lafayette IN USA

ISBN: (纸本)9781424423019

Collaborative sensing of spectral occupancy can increase accuracy and relax the required sensitivity of individual sensing units. Collaborative sensing requires knowledge about the densities of collected sensing statistics to form the correct decision statistics for the optimum likelihood ratio test. In this paper, a parametric density estimation scheme using the expectation-maximization (EM) algorithm is proposed to estimate the parameters of densities that are drawn from a given family. When the log-likelihood function for the EM algorithm satisfies a certain condition, the maximization procedure is shown to require only a weighted sum of the collected sensing statistics. Numerical examples show that in various scenarios the proposed EM algorithm produces more accurate estimates than the sample average does.

关键词： expectation-maximisation algorithm likelihood ratio test Sample mean log-likelihood function Parametric

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A geometric learning approach on the space of complex covariance matrices

A geometric learning approach on the space of complex covari...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Hatem Hajri Salem Said Lionel Bombrun Yannick Berthoumieu Laboratoire IMS (CNRS-UMR 5218) Universite de Bordeaux France

ISBN: (纸本)9781509041183

Many signal and image processing applications, including SAR polarimetry and texture analysis, require the classification of complex covariance matrices. The present paper introduces a geometric learning approach on the space of complex covariance matrices based on a new distribution called Riemannian Gaussian distribution. The proposed distribution has two parameters, the centre of mass Y and the dispersion parameter σ. After having derived its maximum likelihood estimator and its extension to mixture models, we propose an application to texture recognition on the VisTex database.

关键词： Classification Hermitian matrices Information geometry Riemannian centre of mass EM algorithm Information geometry Hermitian matrix variance covariance matrix expectation-maximisation algorithm Covariance matrix New records maximum likelihood estimation texture analysis

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Probabilistic Low-Rank Subspace Clustering 12

Probabilistic Low-Rank Subspace Clustering

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Annual Conference on Neural Information Processing Systems

作者： S. Derin Babacan Shinichi Nakajima Minh N.Do University of Illinois at Urbana-Champaign Urbana IL 61801 USA Nikon Corporation Tokyo 140-8601 Japan

ISBN: (纸本)9781627480031

In this paper, we consider the problem of clustering data points into low-dimensional subspaces in the presence of outliers. We pose the problem using a density estimation formulation with an associated generative model. Based on this probability model, we first develop an iterative expectation-maximization (EM) algorithm and then derive its global solution. In addition, we develop two Bayesian methods based on variational Bayesian (VB) approximation, which are capable of automatic dimensionality selection. While the first method is based on an alternating optimization scheme for all unknowns, the second method makes use of recent results in VB matrix factorization leading to fast and effective estimation. Both methods are extended to handle sparse outliers for robustness and can handle missing values. Experimental results suggest that proposed methods are very effective in subspace clustering and identifying outliers.

关键词： outliers Subspace Bayes methods Probability Cluster Analysis Handles expectation-maximisation algorithm

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MODIFIED K-MEAN CLUSTERING METHOD OF HMM STATES FOR INITIALIZATION OF BAUM-WELCH TRAINING algorithm

MODIFIED K-MEAN CLUSTERING METHOD OF HMM STATES FOR INITIALI...

引用

European Signal Processing Conference

作者： Pauline Larue Pierre Jallon Bertrand Rivet CEA LETI - MINATEC Campus GIPSA-lab Grenoble University

Hidden Markov models are widely used for recognition algorithms (speech, writing, gesture,...). In this paper, a classical set of models is considered: state space of hidden variable is discrete and observation probabilities are modeled as Gaussian distributions. The models parameters are generally estimated with training sequences and the Baum-Welch algorithm, i.e. an expectation maximization algorithm. However this kind of algorithm is well known to be sensitive to its initialization point. The problem of this initialization point choice is addressed in this paper: a model with a very large number of states which describe training sequences with accuracy is first constructed. The number of states is then reduced using a k-mean algorithm on the state. This algorithm is compared to other methods based on a k-mean algorithm on the data with numerical simulations.

关键词： Hidden Markov models initialization algorithms training sequence K-means algorithm expectation-maximisation algorithm Training Gaussian distribution Cluster Analysis

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Joint Channel Tracking and Symbol Detection for MIMO-OFDM Mobile Communications

Joint Channel Tracking and Symbol Detection for MIMO-OFDM Mo...

引用

IEEE Vehicular Technology Conference

作者： Xiao-ying Zhang Wei Shao Ji-bo Wei Insiue of Eecronic nd Engineering Nion Universiy of Defense Technoogy Chngsh Chin

ISBN: (纸本)9781424402632

In this paper, a new joint channel tracking and symbol detection algorithm is proposed for MIMO-OFDM systems over the time-varying frequency-selective fading channel. The iterative detection/decoding and channel estimation are combined to improve system performance. With the aid of the candidate list from list sphere decoder (LSD), the channel frequency response of each subcarrier is estimated by the recursive expectation-maximization (EM) algorithm which uses sequential processing over all the significant symbol combinations in the limited candidate list. Moreover, with the prior knowledge of the second-order statistics of the fading channel in time domain, the time-varying channel impulse responses are tracked by the Kalman predictor based on the estimated channel frequency response. Simulation results demonstrate that the proposed algorithm can track the time-varying channel effectively and offer substantial performance gains over the conventional soft decision-directed Kalman filtering techniques.

关键词： Kalman filters MIMO communication OFDM modulation channel estimation decoding expectation-maximisation algorithm fading channels Kalman predictor MIMO-OFDM mobile communication frequency-selective fading channel iterative decoding iter

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GTM Mixture through time for sequential data

GTM Mixture through time for sequential data

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International Joint Conference on Neural Networks

作者： Rakia Jaziri Faicel Chamroukhi Mustapha Lebbah Younes Bennani Data Science Department - LINCOLN Consulting group Lab of mathematics Paul Painleve - UMR CNRS 8524 and the Information Sciences and Systems Lab - UMR CNRS 7296 The Computer Science Lab of Paris Nord - UMR CNRS 7030

ISBN: (纸本)9781509006212

Generative Topographic Mapping (GTM) is a popular probabilistic framework for modeling non-linear relationships in high-dimensional data as well as for unsupervised learning and visualization of such data. It is also known as to provide a principled probabilistic alternative to the well-known Self-Organizing Map (SOM) in the neural networks community, thanks to its flexible mixture model formulation and the desirable properties of the expectation-maximization (EM) algorithm. However, much attention has been focused on the use of GTM for multivariate data, in general assumed to be independent and identically distributed (i.i.d) and the problem of modeling sequences using GTM is less investigated. In this paper, we focus on GTM for unsupervised modeling and visualization of sequential data. We consider modeling sequences of continuous multidimensional observations and we propose a GTM through time (GTM-TT) approach based on hidden Markov models (HMM) where the observations are a sent of independent sequences, rather than a signle sequence. We further extend the model to the clustering of multiple sequences by proposing a GTM-TT mixture model. The model parameters are estimated by maximum likelihood via the EM algorithm. The proposed approach is evaluated using simulated data and real-world data.

关键词： GANG TEMPERATURE MONITOR expectation-maximisation algorithm Visualization Hidden Markov models Unsupervised learning

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Reasoning Visual Dialogs with Structural and Partial Observations

Reasoning Visual Dialogs with Structural and Partial Observa...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Zilong Zheng Wenguan Wang Siyuan Qi Song-Chun Zhu University of California Los Angeles Inception Institute of Artificial Intelligence

ISBN: (纸本)9781728132945

We propose a novel model to address the task of Visual Dialog which exhibits complex dialog structures. To obtain a reasonable answer based on the current question and the dialog history, the underlying semantic dependencies between dialog entities are essential. In this paper, we explicitly formalize this task as inference in a graphical model with partially observed nodes and unknown graph structures (relations in dialog). The given dialog entities are viewed as the observed nodes. The answer to a given question is represented by a node with missing value. We first introduce an expectation Maximization algorithm to infer-both the underlying dialog structures and the missing node values (desired answers). Based on this, we proceed to propose a differentiable graph neural network (GNN) solution that approximates this process. Experiment results on the VisDial and VisDial-Q datasets show that our model outperforms comparative methods. It is also observed that our method can infer the underlying dialog structure for better-dialog reasoning.

关键词： Conversation expectation-maximisation algorithm missing value Answer Reasoning Line of nodes

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Localization of Multiple Sources from a Binaural Head in a Known Noisy Environment

Localization of Multiple Sources from a Binaural Head in a K...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Alban Portello Gabriel Bustamante Patrick Danes Alexis Mifsud CNRS LAAS 7 avenue du colonel Roche F-31400 Toulouse France

ISBN: (纸本)9781479969357

This paper presents a strategy to the localization of multiple sound sources from a static binaural head. The sources are supposed W-Disjoint Orthogonal and their number is assumed known. Their most likely azimuths are computed by means of the expectation-Maximization algorithm. Application of the method on simulated data is reported, as well as some evaluations of its HARK implementation on experimental data. Two important properties are observed: scattering effects can be coped with, thanks to the required prior knowledge of the (room-independent) head interaural transfer function;the environment noise statistics are handled separately.

关键词： Noisy environment Head simulated data expectation-maximisation algorithm acoustic radiators

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The variational hierarchical EM algorithm for clustering hidden Markov models 12

The variational hierarchical EM algorithm for clustering hid...

引用

Annual Conference on Neural Information Processing Systems

作者： Emanuele Coviello Antoni B. Chan Gert R.G. Lanckriet ECE Dept. UC San Diego CS Dept. CityU of Hong Kong

ISBN: (纸本)9781627480031

In this paper, we derive a novel algorithm to cluster hidden Markov models (HMMs) according to their probability distributions. We propose a variational hierarchical EM algorithm that i) clusters a given collection of HMMs into groups of HMMs that are similar, in terms of the distributions they represent, and ii) characterizes each group by a "cluster center", i.e., a novel HMM that is representative for the group. We illustrate the benefits of the proposed algorithm on hierarchical clustering of motion capture sequences as well as on automatic music tagging.

关键词： Hidden Markov models expectation-maximisation algorithm Hierarchical motion capture Probability distribution Cluster Analysis

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AN INTEGRATION OF SOURCE LOCATION CUES FOR SPEECH CLUSTERING IN DISTRIBUTED MICROPHONE ARRAYS

AN INTEGRATION OF SOURCE LOCATION CUES FOR SPEECH CLUSTERING...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Mehrez Souden Keisuke Kinoshita Tomohiro Nakatani NTT Communication Science Laboratories Kyoto Japan

ISBN: (纸本)9781479903573

We propose a new approach for clustering competing speech sources using distributed microphone arrays. In this approach, we first define two feature vectors where the first captures the intra-node location information while the second captures the level difference of speech energy recorded at different nodes. Then, we introduce Watson and Dirichlet mixture models to model the first and second features, respectively. We integrate both types of information in an expectation maximization algorithm to cluster the simultaneous speech sources. The performance of the proposed approach is superior to best node selection and comparable to centralized processing in terms of conventional blind source separation metrics.

关键词： Speech expectation-maximisation algorithm Position measurement Microphone Device Component centralized processing microphone arrays POSITION INFORMATION Eigenvector information-type integration Cluster Analysis

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