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

共 270 条记录，以下是181-190 订阅

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Multichannel nonnegative matrix factorization in convolutive mixtures. With application to blind audio source separation

Multichannel nonnegative matrix factorization in convolutive...

引用

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

作者： Alexey Ozerov Cedric Fevotte Institut TELECOM CNRS LTCI TELECOM ParisTech Paris France CNRS LTCI TELECOM ParisTech Paris France

We consider inference in a general data-driven object-based model of multichannel audio data, assumed generated as a possibly under-determined convolutive mixture of source signals. Each source is given a model inspired from nonnegative matrix factorization (NMF) with the Itakura-Saito divergence, which underlies a statistical model of superimposed Gaussian components. We address estimation of the mixing and source parameters using two methods. The first one consists of maximizing the exact joint likelihood of the multichannel data using an expectation-maximization algorithm. The second method consists of maximizing the sum of individual likelihoods of all channels using a multiplicative update algorithm inspired from NMF methodology. Our decomposition algorithms were applied to stereo music and assessed in terms of blind source separation performance.

关键词： Source separation Telecommunications Frequency estimation Blind source separation Tensile stress Inference algorithms Signal generators expectation-maximization algorithms Matrix decomposition Filters

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Robust Face Matching Under Large Occlusions

Robust Face Matching Under Large Occlusions

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International Conference on Image Analysis and Processing

作者： Georgy Gimel'farb Patrice Delmas John Morris Al Shorin Department of Computer Science University of Auckland Auckland New Zealand

Outliers due to occlusions and contrast and offset signal deviations notably hinder recognition and retrieval of facial images. We propose a new maximum likelihood matching score with "soft masking" of outliers which is robust in these conditions. Differences between two images are modelled by unknown contrast and offset deviations from an unknown template and by independent pixel-wise errors. The error distribution is a mixture of a zero-centred Gaussian noise with an unknown variance and uniformly distributed outliers. The matching score combines the maximum likelihood estimates of model parameters and the soft masks being produced by a simple iterative expectation-maximisation algorithm. Experiments with facial images from the MIT face database show the robustness of this technique in the presence of large occlusions.

关键词： Maximum likelihood estimation Noise robustness Face recognition Image recognition Image retrieval Pixel Gaussian noise Parameter estimation expectation-maximization algorithms Image databases

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A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIMATION IN WIRELESS SENSOR NETWORKS

A DIFFUSION-BASED DISTRIBUTED EM ALGORITHM FOR DENSITY ESTIM...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Silvana Silva Pereira Alba Pages-Zamora Roberto Lopez-Valcarce SPCOM Group Universitat Politecnica de Catalunya-Barcelona Tech GPSC Universidade de Vigo

ISBN: (纸本)9781479903573

Distributed implementations of the expectation-maximization (EM) algorithm reported in literature have been proposed for applications to solve specific problems. In general, a primary requirement to derive a distributed solution is that the structure of the centralized version enables the computation involving global information in a distributed fashion. This paper treats the problem of distributed estimation of Gaussian densities by means of the EM algorithm in wireless sensor networks using diffusion strategies, where the information is gradually diffused across the network for the computation of the global functions. The low-complexity implementation presented here is based on a two time scale operation for information averaging and diffusion. The convergence to a fixed point of the centralized solution has been studied and the appealing results motivate our choice for this model. Numerical examples provided show that the performance of the distributed EM is, in practice, equal to that of the centralized scheme.

关键词： Distributed algorithms expectation-maximization algorithms Gaussian distribution Unsupervised learning Wireless sensor networks expectation-maximisation algorithm Wireless sensor networks Unsupervised learning distributed algorithms Gaussian distribution Distribute Radio

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A robust predominant-F0 estimation method for real-time detection of melody and bass lines in CD recordings

A robust predominant-F0 estimation method for real-time dete...

引用

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

作者： M. Goto Electro Technical Laboratory Tsukuba Ibaraki Japan

This paper describes a robust method for estimating the fundamental frequency (F0) of melody and bass lines in monaural real-world musical audio signals containing sounds of various instruments. Most previous F0-estimation methods had great difficulty dealing with such complex audio signals because they were designed to deal with mixtures of only a few sounds. To make it possible to estimate the F0 of the melody and bass lines, we propose a predominant-F0 estimation method called PreFEst that does not rely on the F0's unreliable frequency component and obtains the most predominant F0 supported by harmonics within an intentionally limited frequency range. It evaluates the relative dominance of every possible F0 by using the expectation-maximization algorithm and considers the temporal continuity of F0s by using a multiple-agent architecture. Experimental results show that our real-time system can detect the melody and bass lines in audio signals sampled from commercially distributed compact discs.

关键词： Robustness Frequency estimation Instruments CD recording Real time systems Acoustic noise Audio recording Laboratories expectation-maximization algorithms Computer architecture

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Motion-based video segmentation using fuzzy clustering and classical mixture model

Motion-based video segmentation using fuzzy clustering and c...

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IEEE International Conference on Image Processing

作者： S. Nitsuwat J.S. Jin H.M. Hudson School of Computer Science and Engineering University of New South Wales Sydney NSW Australia Department of Statistics Macquarie University Sydney NSW Australia

ISBN: (纸本)0780362977

Motion-based segmentation plays an important role in dynamic scene analysis of video sequences. We present a scheme for extracting moving objects. First, three different resolutions of the dense optical flow fields are calculated using a complex discrete wavelet transform. Surface fitting of all levels of these vectors is then performed over the affine parametric motion model. Next, the clustering by the competitive agglomeration algorithm is applied in the parameter space of the coarsest level. The results of this step are the optimum number of clusters and the center of each cluster. Using information from the previous level, the parameter spaces of the following levels are then segmented using the classical mixture model and the expectation-maximization algorithm. Finally, the individual moving object and background are represented in layers. Experimental results showing the significance of this proposed method are provided.

关键词： Discrete wavelet transforms Surface fitting Motion analysis Image analysis Video sequences Nonlinear optics Image motion analysis Optical surface waves Clustering algorithms expectation-maximization algorithms

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Fuzzy expectation-maximisation algorithm for speech and speaker recognition

Fuzzy expectation-maximisation algorithm for speech and spea...

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Conference of the North American Fuzzy Information Processing Society - NAFIPS

作者： D. Tran H. Wagner School of Computing Human-Computer Communication Lab University of Canberra Belconnen ACT Australia

A fuzzy c-means approach to the expectation-maximisation (EM) algorithm is proposed. A family of fuzzy EM algorithms of various degrees of fuzziness is presented, where the EM algorithm is referred to as a fuzzy EM algorithm with degree of fuzziness of one. This fuzzy approach can be applied to EM-style algorithms such as the Baum-Welch algorithm for hidden Markov models, and the EM algorithm for Gaussian mixture models in speech and speaker recognition. The fuzzy EM algorithm for Gaussian mixture models is considered in detail as a demonstration for applying the fuzzy EM algorithm.

关键词： expectation-maximization algorithms Speech Speaker recognition Clustering algorithms Hidden Markov models Iterative algorithms Inference algorithms Maximum likelihood estimation Biological system modeling Extraterrestrial measurements

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A Novel Model-based Method for Feature Extraction from Protein Sequences for Classification

A Novel Model-based Method for Feature Extraction from Prote...

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IEEE Signal Processing and Communications Applications (SIU)

作者： Sarac Atalay Bilgisayar Mühendisliği Bölümü Orta Doğu Teknik Üniversitesi Ankara Turkey

Representation of amino-acid sequences constitutes the key point in classification of proteins into functional or structural classes. The representation should contain the biologically meaningful information hidden in the primary sequence of the protein. Conserved or similar subsequences are strong indicators of functional and structural similarity. In this study we present a feature mapping that takes into account the models of the subsequences of protein sequences. An expectation-maximization algorithm along with an HMM mixture model is used to cluster and learn the models of subsequences of a given set of proteins

关键词： Feature extraction Proteins Hidden Markov models expectation-maximization algorithms Influenza

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A Novel Data Statistical Analysis Method Based on Hyper-Erlang Distribution

A Novel Data Statistical Analysis Method Based on Hyper-Erla...

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Computer, Big Data and Artificial Intelligence (ICCBD+AI), International Conference on

作者： Dan Yang Mengjun Li College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (数字)9798350373233

ISBN: (纸本)9798350373240

Selecting the appropriate probability distribution can be a challenging task when using parametric methods for statistical analysis of specific data. To address this challenge, this paper proposes a method of analyzing the data using the Hyper-Erlang distribution. This distribution is a dense subset of all probability distributions on the non-negative real axis and is both easy to analyze and compute. This paper discusses the dense properties of the Hyper-Erlang distribution and the parameter estimation method based on the expectation maximization algorithm, and verifies the validity of the proposed method using the actual income data.

关键词： Parameter estimation Fitting Big Data Probability distribution Task analysis expectation-maximization algorithms Artificial intelligence

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On Possibilistic Clustering algorithms Based on Noise Clustering

On Possibilistic Clustering Algorithms Based on Noise Cluste...

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International Symposium on Soft Computing and Intelligent Systems (SCIS)

作者： Yuchi Kanzawa Department of Communications Engineering Shibaura Institute of Technology Tokyo JAPAN

ISBN: (纸本)9781509026791

In this study, several possibilistic clustering methods are proposed based on noise clustering. The proposed methods are motivated by the fact that conventional possibilistic clustering methods do not correspond with noise clustering methods in entropy-regularized situations, whereas these methods do correspond in Bezdek-type fuzzified situation.

关键词： Clustering methods Optimization Entropy Clustering algorithms Mixture models Intelligent systems expectation-maximization algorithms

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A Probabilistic Ensemble Pruning Algorithm

A Probabilistic Ensemble Pruning Algorithm

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Huanhuan Chen Peter Tino Xin Yao School of Computer Science University of Binningham Birmingham UK

An ensemble is a group of learners that work together as a committee to solve a problem. However, the existing ensemble training algorithms sometimes generate unnecessary large ensembles, which consume extra computational resource and may degrade the performance. Ensemble pruning algorithm aims to find a good subset of ensemble members to constitute a small ensemble, which saves the computational resource and performs as well as, or better than, the non-pruned ensemble. This paper introduces a probabilistic ensemble pruning algorithm by choosing a set of "sparse" combination weights, most of which are zero, to prune the large ensemble. In order to obtain the set of sparse combination weights and satisfy the non-negative restriction of the combination weights, a left-truncated, non-negative, Gaussian prior is adopted over every combination weight. expectation-maximization algorithm is employed to obtain maximum a posterior (MAP) estimation of weight vector. Four benchmark regression problems and another four benchmark classification problems have been employed to demonstrate the effectiveness of the method

关键词： Machine learning Degradation Bayesian methods Gaussian distribution Computer science expectation-maximization algorithms Bagging Boosting Evolutionary computation Machine learning algorithms

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