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

共 493 条记录，以下是471-480 订阅

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A Probabilistic Framework for Deep Learning 16

A Probabilistic Framework for Deep Learning

引用

Annual Conference on Neural Information Processing Systems

作者： Ankit B. Patel Tan Nguyen Richard G. Baraniuk Baylor College of Medicine Rice University Rice University

ISBN: (纸本)9781510838819

We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our framework provides new insights into the successes and shortcomings of DCNs as well as a principled route to their improvement. DRMM training via the expectation-Maximization (EM) algorithm is a powerful alternative to DCN back-propagation, and initial training results are promising. Classification based on the DRMM and other variants outperforms DCNs in supervised digit classification, training 2-3 x faster while achieving similar accuracy. Moreover, the DRMM is applicable to semi-supervised and unsupervised learning tasks, achieving results that are state-of-the-art in several categories on the MNIST benchmark and comparable to state of the art on the CIFAR10 benchmark.

关键词： Frameworks Probability state of the art BENCHMARKS Machine Learning initial training Arts Unsupervised learning expectation-maximisation algorithm

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An EM-based estimation for a two-level traitor tracing scheme

An EM-based estimation for a two-level traitor tracing schem...

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： Faten Chaabane Maha Charfeddine William Puech Chokri Ben Amar REsearch Groups in Intelligent Machines ENIS Sfax 3038 Tunisia LIRMM Laboratory UMR 5506 CNRS University of Montpellier161 Rue Ada 34392 Montpellier Cedex 05 France

ISBN: (纸本)9781509018987

In multimedia distribution platforms, one of the main challenges is to provide an efficient and accurate tracing process despite the lack of information about the colluders' strategy. Indeed, the original Tardos tracing performance is considered as suboptimal because of its agnostic behavior and conservative accusation regardless the collusion strategy. The expectation Maximization algorithm has shown to be an efficient solution to estimate the collusion channel and thus to tune the Tardos accusation functions. In this paper, we explore the impact of this algorithm in a group-based tracing scheme to deal with the computational costs and the invariance of the Tardos accusation performance. The tracing scheme we propose benefits from a twofold accusation process. Indeed, in a first time, it is based on a two-level tracing strategy which consists in tracing guilty groups in a first level with the Boneh Shaw tracing code and in retrieving at least one colluder in accused groups with Tardos code in the second level. This strategy has reduced efficiently the decoding process of the Tardos code. The main shift we propose in the second level is to apply the expectation Maximization algorithm to be tightly tied to collusion yielded by colluders and hence to find the more accurate Tardos accusation functions. The performance of the resulting tracing scheme is evaluated according to different criteria and promising results have been achieved when compared to the existing tracing schemes proposed in the literature.

关键词： main challenges colluders' strategy expectation Maximization RAPPING expectation-maximisation algorithm Scheme

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UNSUPERVISED CHANGE DETECTION WITH HIGH-RESOLUTION SAR IMAGES BY EDGE-PRESERVING MARKOV RANDOM FIELDS AND GRAPH-CUTS

UNSUPERVISED CHANGE DETECTION WITH HIGH-RESOLUTION SAR IMAGE...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Gabriele Moser Sebastiano B. Serpico University of Genoa Dept. of Naval Electrical Electronic and Telecommunications Eng. (DITEN)

ISBN: (纸本)9781467311601

Change detection techniques represent important tools for environmental monitoring and damage assessment after environmental disasters. However, change detection methods that were found accurate for coarser-resolution SAR are often ineffective with current very high resolution (VHR) satellite SAR due to the need to suitably model the contextual and geometrical information associated with VHR data. In this paper, a novel unsupervised change detection technique is proposed for VHR SAR based on Markov random fields (MRFs), line processes, and a dictionary of SAR-specific probability density models. The estimation of the parameters of the proposed MRF model is carried out through the expectation-maximization algorithm and the method of log-cumulants. Graph cuts are used to minimize the energy function of the MRF model because of their capability to approach global minima or strong local minima in acceptable computation times. The proposed method is experimented with COSMO-SkyMed images acquired before and after an earthquake.

关键词： Unsupervised change detection Markov random fields Line processes Graph cuts expectation-maximization Method of log-cumulants Synthetic aperture radar Specific absorption rate Rescue SAFETY ANALYSIS REPORTS High definition images inspection technique expectation-maximisation algorithm damage assessment Parameter estimation Dictionaries as Topic environmental monitoring inspection methods Earthquakes

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EMDC: A Semi-supervised Approach for Word Alignment 10

EMDC: A Semi-supervised Approach for Word Alignment

引用

International conference on computational linguistics

作者： Qin Gao Francisco Guzman Stephan Vogel Carnegie Mellon University Centro de Sistemas Inteligentes

ISBN: (纸本)9787302234562

Abstract This paper proposes a novel semisupervised word alignment technique called EMDC that integrates discriminative and generative methods. A discriminative aligner is used to find high precision partial alignments that serve as constraints for a generative aligner which implements a constrained version of the EM algorithm. Experiments on small-size Chinese and Arabic tasks show consistent improvements on AER. We also experimented with moderate-size Chinese machine translation tasks and got an average of 0.5 point improvement on BLEU scores across five standard NIST test sets and four other test sets.

关键词： Belgo-Luxembourg Economic Union ANION EXCHANGE RESIN TEST SETS Word Religious Missions Alignment expectation-maximisation algorithm Chinese machine translation Chinese philosophy Chinese art

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fMRI Time-Series Clustering Using a Mixture of Mixtures of Student's-t and Rayleigh Distributions

fMRI Time-Series Clustering Using a Mixture of Mixtures of S...

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European Signal Processing Conference

作者： Qunyi Xie Zhuyan Zhang Xu Pan Hongqing Zhu School of Information Science & Engineering East China University of Science and Technology

ISBN: (纸本)9781509018918

In this paper, a new Markov random field-based mixture model, where each of its components is a mixture of Student's-t and Rayleigh distributions, is proposed for clustering fMRI time-series. By introducing the non-symmetric Rayleigh distribution, the proposed algorithm has flexibility to fit various types of observed time-series. Moreover, our method incorporates Markov random field so that the spatial relationships between neighboring voxels are considered, which makes the presented model more robust to noise, and that preserves more details of the clustering results compared with other symmetric distribution-based algorithms. Additionally, the expectation maximization algorithm is directly implemented to estimate the parameter set by maximizing the data log-likelihood function. The proposed framework is evaluated on real fMRI time-series, and the quantitatively compared results are demonstrated in terms of effectiveness and accuracy.

关键词： mixtures Rayleigh distribution fMRI expectation-maximisation algorithm Rayleigh Markov random fields Cluster Analysis Data logging Flexibility log-likelihood function

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Collaborative PLSA for Multi-View Clustering

Collaborative PLSA for Multi-View Clustering

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International Conference on Pattern Recognition

作者： Yu Jiang Jing Liu Zechao Li Hanqing Lu National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781467322164

Data has multi-view representations from various feature spaces in real world. Multi-view clustering algorithms allow leveraging information from multiple views of the data and this may substantially improve the clustering result obtained by using a single view. In this paper, we propose a novel algorithm called Collaborative PLSA (C-PLSA)for multi-view clustering, which works on the assumption that the clustering from one view should agree with the clustering from another view. The proposed C-PLSA combines individual PLSA models on two different views, and imports a regularizer to force the both clustering results agree across the two views. To solve the regularized problem, an alternative optimization algorithm based on generalized EM (GEM) is adopted for maximum likelihood estimation. Experiments on two real-world datasets, i.e., Reuters multilingual text and Corel images, demonstrate the improved performance of our proposed method over some related work.

关键词： expectation-maximisation algorithm optimisation pattern clustering

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A vessel segmentation method for MRA data based on probabilistic mixture model

A vessel segmentation method for MRA data based on probabili...

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2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015)

作者： Pei Lu Cheng Wang Shoujun Zhou Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Accurate segmentation and visualization of cerebral vessels have important significance to the diagnosis and treatment of relevant brain diseases. However, some segmentation algorithms are only competent for the standard data. In this paper, a segmentation method based on probabilistic mixture model is proposed to solve clinical problems. Through histogram analysis of the magnetic resonance angiography (MRA) data offered by Guangzhou General Hospital of the Chinese PLA, a mixture model formed by six probabilistic distributions (one Exponential, one Rayleigh, and four Normal distributions) was built to fit the histogram curve. Least squares and expectation maximization (EM) algorithms have been used for parameters estimation. At last, the segmentation was enhanced by maximum a posteriori probability (MAP) and Markov random field (MRF) algorithm. The effectiveness of the proposed method has been validated by segmentation tests on a series of clinical MRA data with good performance.

关键词： EM algorithm MRF Vessel segmentation histogram analysis mixture model Mixture models expectation-maximisation algorithm Probability histograms MYRF gene Maximum a posteriori estimation Parameter estimation cerebrovascular system

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A SPARSE COMPONENT MODEL OF SOURCE SIGNALS AND ITS APPLICATION TO BLIND SOURCE SEPARATION

A SPARSE COMPONENT MODEL OF SOURCE SIGNALS AND ITS APPLICATI...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Yu Kitano Hirokazu Kameoka Yosuke Izumi Nobutaka Ono Shigeki Sagayama Graduate School of Information Science and Technology The University of Tokyo Japan NTT Communication Science Laboratories NTT Corporation

ISBN: (纸本)9781424442959

In this paper, we propose a new method of blind source separation (BSS) for music signals. Our method has the following characteristics: 1) the method is a combination of the sparseness-based model of source signals and the factorized basis model in nonnegative matrix factorization (NMF), 2) it is assumed that only one basis which structure source signals is active at each time-frequency bin of the observed signals, in order to degrade the degree of freedom, 3) parameter estimation algorithm is based on the EM algorithm regarding the index of the only one active basis as the hidden variable. We develop the formulation at a different point from NMF and show source separation performance in some simulation experiments.

关键词： Blind source separation nonnegative matrix factorization EM algorithm sparseness of source signals time-frequency masking Blind source separation expectation-maximisation algorithm signals matrix decomposition Source separation Phase Mask NMF protocol

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PERMUTATION-FREE CONVOLUTIVE BLIND SOURCE SEPARATION VIA FULL-BAND CLUSTERING BASED ON FREQUENCY-INDEPENDENT SOURCE PRESENCE PRIORS

PERMUTATION-FREE CONVOLUTIVE BLIND SOURCE SEPARATION VIA FUL...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Nobutaka Ito Shoko Araki Tomohiro Nakatani NTT Communication Science Laboratories Nippon Telegraph and Telephone Corporation

ISBN: (纸本)9781479903573

We propose permutation-free frequency-domain blind source separation (BSS) via full-band clustering of the time-frequency (T-F) components based on time-varying signal presence priors. Frequency-domain methods of BSS usually process each frequency bin separately, and therefore necessitate the subsequent alignment of the permutation ambiguity that arises between frequency bins. In contrast, the proposed method simultaneously processes all frequency bins by using a mixture model with time-varying, frequency-independent mixture weights. We propose to assume non-sparse priors on the mixture weights to prevent the degradation of source separation performance by the time-varying mixture weights. We propose a customized expectation-maximization (EM) algorithm for the maximum a posteriori (MAP) estimation of the model parameters, to which we introduce a novel technique to avoid convergence to local maxima. For audio source separation, we use the normalized observation vector as the feature vector, and the Watson mixture model (WMM) as the mixture model. Evaluations confirm that the proposed permutation-free BSS results in source separation performance comparable to the state-of-the-art clustering-based BSS composed of bin-wise clustering and permutation alignment.

关键词： Blind source separation Clustering EM algorithm Mixture model Permutation problem Blind source separation expectation-maximisation algorithm Source separation Mixture models Cluster Analysis Eigenvector time varying

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UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

UNIVERSAL WYNER-ZIV CODING FOR GAUSSIAN SOURCES

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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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