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

共 493 条记录，以下是401-410 订阅

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A SOLUTION TO THE WATERMARK DETECTION PROBLEM BASED ON BAYESIAN ESTIMATION AND EM algorithm

A SOLUTION TO THE WATERMARK DETECTION PROBLEM BASED ON BAYES...

引用

European signal processing conference

作者： Akio Miyazaki Faculty of Information Science Kyushu Sangyo University Fukuoka 813-8503 Japan

ISBN: (纸本)9781617388767

We have recently discussed digital watermarking techniques based on modifying the spectral coefficient of an image, and have presented a model of the watermark embedding and extracting processes and a model of watermark distortion caused by image processing and attack of watermarked images. In this paper, based on these models, we formulate the watermark detection problem as a kind of blind deconvolution problem. Then, as a solution to this problem, we propose a watermark detection method using Bayesian estimation and EM algorithm.

关键词： Bayes methods expectation-maximisation algorithm Digital watermarking Image processing

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Learning dictionary for efficient signal compression

Learning dictionary for efficient signal compression

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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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A GENERATIVE SEMI-SUPERVISED MODEL FOR MULTI-VIEW LEARNING WHEN SOME VIEWS ARE LABEL-FREE

A GENERATIVE SEMI-SUPERVISED MODEL FOR MULTI-VIEW LEARNING W...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Gaole Jin Raviv Raich David J. Miller School of EECS Oregon State University EE Dept. The Pennsylvania State University

ISBN: (纸本)9781479903573

We consider multi-view classification for the challenging scenario where, for some views, there are no labeled training examples. Several discriminative approaches have been recently proposed for special instances of this problem. Here, alternatively, we propose a generative semi-supervised mixture model across all views which, via marginalization, flexibly performs exact class inference, given any subset of available views. The proposed model is an extension of semi-supervised mixtures to a multi-view setting, as well as a semi-supervised extension of mixtures of factors analyzers (MFA)[1]. A novel EM algorithm with a computationally efficient E-step is derived for learning our multi-view model. Specialization of this formulation to the standard MFA problem also gives a reduced complexity E-step, compared to the original EM algorithm proposed for MFA. Our multi-view method is experimentally demonstrated on digit recognition using audio and lip video views, achieving competitive results with alternative, discriminative approaches.

关键词： Multi-view learning Semi-supervised learning Mixture of factors analyzers expectation-Maximization Semi-supervised Learning expectation-maximisation algorithm Learning multifibre agreement View Audio mixtures

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Estimating Mixture Models via Mixtures of Polynomials 15

Estimating Mixture Models via Mixtures of Polynomials

引用

Annual Conference on Neural Information Processing Systems

作者： Sida I. Wang Arun Tejasvi Chaganty Percy Liang Computer Science Department Stanford University Stanford CA 94305

ISBN: (纸本)9781510825024

Mixture modeling is a general technique for making any simple model more expressive through weighted combination. This generality and simplicity in part explains the success of the expectation Maximization (EM) algorithm, in which updates are easy to derive for a wide class of mixture models. However, the likelihood of a mixture model is non-convex, so EM has no known global convergence guarantees. Recently, method of moments approaches offer global guarantees for some mixture models, but they do not extend easily to the range of mixture models that exist. In this work, we present Polymom, an unifying framework based on method of moments in which estimation procedures are easily derivable, just as in EM. Polymom is applicable when the moments of a single mixture component are polynomials of the parameters. Our key observation is that the moments of the mixture model are a mixture of these polynomials, which allows us to cast estimation as a Generalized Moment Problem. We solve its relaxations using semidefinite optimization, and then extract parameters using ideas from computer algebra. This framework allows us to draw insights and apply tools from convex optimization, computer algebra and the theory of moments to study problems in statistical estimation. Simulations show good empirical performance on several models.

关键词： Polynomial Moment of force Mixture models mixtures METHOD OF MOMENTS Symbolic algebra Non-Convex expectation-maximisation algorithm

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Switch mechanism diagnosis using a pattern recognition approach

Switch mechanism diagnosis using a pattern recognition appro...

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2008 4th IET International Conference on Railway Condition Monitoring

作者： F. Chamroukhi A. Same P. Aknin M. Antoni INRETS/LTN Arcueil France SNCF Direction de l'Infrastructure Paris France

An original pattern recognition approach for the diagnosis of switch mechanisms driven by an electric motor is presented in this paper. Its main advantage is that it does not require a physical model of the system and can easily be adapted to other complex systems. The available data for this task are the signals of the electrical power consumption during the switch actuation period and the proposed method consists of two steps: the feature extraction from the signals and the recognition of different operating states (class without defect, class with minor defect and class with critical defect) using mixture discriminant analysis (MDA). This method assumes the classes to be represented by a Gaussian mixture distribution whose parameters are estimated by the maximum likelihood method, using the expectation-maximization (EM) algorithm. An experimental study performed on real measured signals covering a wide range of defects reveals some good performances of the proposed approach compared to others classification methods such as K-Nearest-Neighbors, Neural Networks and the classical Bayesian discriminant approach (with one Gaussian distribution per class).

关键词： Condition monitoring Diagnosis Switch mechanism Pattern recognition Gaussian mixture model EM algorithm Pattern recognition expectation-maximisation algorithm Condition monitoring Gaussian distribution Switches Diagnosis maximum likelihood pattern recognition method signals Mixture models electrical power consumption Electric motors switching mechanism

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A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

A PROBABILISTIC FRAMEWORK FOR MULTIPLE SPEAKER LOCALIZATION

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Youssef Oualil Mathew Magimai.-Doss Friedrich Faubel Dietrich Klakow Spoken Language Systems Saarland University Idiap Research Institute

ISBN: (纸本)9781479903573

This paper presents a novel probabilistic framework for localizing multiple speakers with a microphone array. In this framework, the generalized cross correlation function (GCC) of each microphone pair is interpreted as a probability distribution of the time difference of arrival (TDOA) and subsequently approximated as a Gaussian mixture. The distribution parameters are estimated with a weighted expectation maximization algorithm. Then, the joint distribution of the TDOA Gaussian mixtures is mapped to a multimodal distribution in the location space, where each mode represents a potential source location. The approach taken here performs the localization by 1) reducing the search space to some regions that are likely to contain a source and then 2) extracting the actual speaker locations with a numerical optimization algorithm. The effectiveness of the proposed approach is shown using the AV16.3 corpus.

关键词： Microphone arrays Localization Multiple speakers Gaussian mixture Steered response power microphone arrays Loudspeakers Time difference of arrival expectation-maximisation algorithm Probability distribution distributed parameter Position measurement TimeDif Optimization algorithms

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AN EM-algorithm APPROACH FOR THE DESIGN OF ORTHONORMAL BASES ADAPTED TO SPARSE REPRESENTATIONS

AN EM-ALGORITHM APPROACH FOR THE DESIGN OF ORTHONORMAL BASES...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： A. Dremeau C. Herzet INRIA Centre Rennes - Bretagne Atlantique Campus universitaire de Beaulieu 35000 Rennes France

ISBN: (纸本)9781424442959

In this paper, we consider the problem of dictionary learning for sparse representations. Several algorithms dealing with this problem can be found in the literature. One of them, introduced by Sezer et al. in [1] optimizes a dictionary made up of the union of orthonormal bases. In this paper, we propose a probabilistic interpretation of Sezer's algorithm and suggest a novel optimization procedure based on the EM algorithm. Comparisons of the performance in terms of missed detection rate show a clear superiority of the proposed approach.

关键词： Sparse representations dictionary learning expectation-maximization algorithm expectation-maximisation algorithm sparse representation Alkali Bases Dictionaries as Topic algorithmic approach representations Optimization Procedure detection rate (DR)

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Fast Large-scale Mixture Modeling with Component-specific Data Partitions 10

Fast Large-scale Mixture Modeling with Component-specific Da...

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Annual Conference on Neural Information Processing Systems

作者： Bo Thiesson Chong Wang Microsoft Research Princeton University

ISBN: (纸本)9781617823800

Remarkably easy implementation and guaranteed convergence has made the EM algorithm one of the most used algorithms for mixture modeling. On the downside, the E-step is linear in both the sample size and the number of mixture components, making it impractical for large-scale data. Based on the variational EM framework, we propose a fast alternative that uses component-specific data partitions to obtain a sub-linear E-step in sample size, while the algorithm still maintains provable convergence. Our approach builds on previous work, but is significantly faster and scales much better in the number of mixture components. We demonstrate this speedup by experiments on large-scale synthetic and real data.

关键词： Sample Size Fasting Speeding partitions large sizes algorithms Mixture models Modeling observational data sublinear Data partitioning Converge expectation-maximisation algorithm

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Learning Marked Markov Modulated Poisson Processes for Online Predictive Analysis of Attack Scenarios

Learning Marked Markov Modulated Poisson Processes for Onlin...

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International Symposium on Software Reliability Engineering (ISSRE)

作者： Laura Carnevali Francesco Santoni Enrico Vicario Department of Information Engineering University of Florence Firenze Italy

ISBN: (纸本)9781728149837

Runtime predictive analysis of quantitative models can support software reliability in various application scenarios. The spread of logging technologies promotes approaches where such models are learned from observed events. We consider a system visiting transient states of a hidden process until reaching a final state and producing observations with stochastic arrival times and types conditioned by visited states, and we abstract it as a marked Markov modulated Poisson Process (MMMPP) with left-to right structure. We present an expectation-Maximization (EM) algorithm that learns the MMMPP parameters from observation sequences acquired in repeated execution of the transient behavior, and we use the model at runtime to infer the current state of the process from actual observed events and to dynamically evaluate the remaining time to the final state. The approach is illustrated using synthetic datasets generated from a stochastic attack tree of the literature enriched with an observation model associating each state with an expected statistics of observation types and arrival times. Accuracy of prediction is evaluated under different variability of hidden states sojourn durations and of the observations arrival process, and compared against previous literature that mainly exploits either the timing or the types of observed events.

关键词： expectation-maximisation algorithm learning (artificial intelligence) Markov processes security of data software reliability expectation-maximisation algorithm Software reliability Data Security Poisson process Markov chain Runtime Markov

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REM-Seg: A Robust EM algorithm for Parallel Segmentation and Registration of Point Clouds

REM-Seg: A Robust EM Algorithm for Parallel Segmentation and...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Benjamin Eckart Alonzo Kelly Robotics Institute Carnegie Mellon University Pittsburgh PA USA

ISBN: (纸本)9781467363563

For purposes of real-time 3D sensing, it is important to be able to quickly register together incoming point cloud data. In this paper, we devise a method to quickly and robustly decompose large point clouds into a relatively small number of meaningful surface patches from which we register new data points. The surface patch representation sidesteps the costly problem of matching points to points since incoming data only need to be compared with the patches. The chosen parametrization of the patches (as Gaussians) leads to a smooth data likelihood function with a well-defined gradient. This representation thus forms the basis for a robust and efficient registration algorithm using a parallelized gradient descent implemented on a GPU using CUDA. We use a modified Gaussian Mixture Model (GMM) formulation solved by expectation Maximization (EM) to segment the point cloud and an annealing gradient descent method to find the 6-DOF rigid transformation between the incoming point cloud and the segmented set of surface patches. We test our algorithm, Robust EM Segmentation (REM-Seg), against other GPU-accelerated registration algorithms on simulated and real data and show that our method scales well to large numbers of points, has a wide range of convergence, and is suitably accurate for 3D registration.

关键词： Parallel Segmentation Registration EM Registration expectation-maximisation algorithm Clouds Large Numbers Patches wide-range Robustness Gauss

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