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

共 495 条记录，以下是421-430 订阅

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Relevance Topic Model for Unstructured Social Group Activity Recognition 13

Relevance Topic Model for Unstructured Social Group Activity...

引用

Annual Conference on Neural Information Processing Systems

作者： Fang Zhao Yongzhen Huang Liang Wang Tieniu Tan Center for Research on Intelligent Perception and Computing Institute of Automation Chinese Academy of Sciences

ISBN: (纸本)9781632660244

Unstructured social group activity recognition in web videos is a challenging task due to 1) the semantic gap between class labels and low-level visual features and 2) the lack of labeled training data. To tackle this problem, we propose a "relevance topic model" for jointly learning meaningful mid-level representations upon bag-of-words (BoW) video representations and a classifier with sparse weights. In our approach, sparse Bayesian learning is incorporated into an undirected topic model (i.e., Replicated Softmax) to discover topics which are relevant to video classes and suitable for prediction. Rectified linear units are utilized to increase the expressive power of topics so as to explain better video data containing complex contents and make variational inference tractable for the proposed model. An efficient variational EM algorithm is presented for model parameter estimation and inference. Experimental results on the Unstructured Social Activity Attribute dataset show that our model achieves state of the art performance and outperforms other supervised topic model in terms of classification accuracy, particularly in the case of a very small number of labeled training videos.

关键词： social group Web video Bayesian learning video data expectation-maximisation algorithm Classifiers linear unit Training Videos state of the art Themes Variational Inference

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FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization

FilterReg: Robust and Efficient Probabilistic Point-Set Regi...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Wei Gao Russ Tedrake Massachusetts Institute of Technology

ISBN: (纸本)9781728132945

Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Point-set registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs). The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks. The video demo and source code are available on our project page.

关键词： ARTICULATED Set of points Registration expectation-maximisation algorithm maximum likelihood estimation Probability Source code Gaussian filter Twisting

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Gaussian-Laplacian Mixture Model for Electricity Market

Gaussian-Laplacian Mixture Model for Electricity Market

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IEEE Annual Conference on Decision and Control

作者： Saahil Shenoy Dimitry Gorinevsky Department of Physics Stanford University Stanford CA 94305 USA Department of Electrical Engineering Stanford University Stanford CA 94305 USA

ISBN: (纸本)9781467360890

This paper develops a statistical modeling and estimation approach combining robust regression and long tail estimation. The approach can be considered as a generalization of Huber regression in robust statistics. A mixture of asymmetric Laplace and Gaussian distributions is estimated using an EM algorithm. The approach estimates the regression model, distribution body, distribution tails, and boundaries between the body and the tails. As an application example, the model is estimated for historical power load data from an electrical utility. Practical usefulness of the model is illustrated by stochastic optimization of electricity order in day-ahead market. The computed optimal policy improves the cost compared to the baseline approach that relies on a normal distribution model.

关键词： power markets Gaussian distribution Mixture models decentralised control statistical models (nuclear) expectation-maximisation algorithm Electric loads ELECTRIC UTILITIES body distribution optimal strategy

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An algorithm for estimating number of components of Gaussian mixture model based on penalized distance

An algorithm for estimating number of components of Gaussian...

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International Conference on Neural Networks and Signal Processing

作者： Daming Zhang Hui Guo Bin Luo Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui University of Science and Technology Hefei China Department of Mathematics & Physics Beijing University of Posts and Telecommunications Hefei China University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781424423101

The expectation-maximization (EM) algorithm is a popular approach for parameter estimation of finite mixture model (FMM). A drawback of this approach is that the number of components of the finite mixture model is not known in advance, nevertheless, it is a key issue for EM algorithms. In this paper, a penalized minimum matching distance-guided EM algorithm is discussed. Under the framework of Greedy EM, a fast and accurate algorithm for estimating the number of components of the Gaussian mixture model (GMM) is proposed. The performance of this algorithm is validated via simulative experiments of univariate and bivariate Gaussian mixture models.

关键词： Finite mixture model Number of components Penalized minimum matching distance Greedy EM Parzen window expectation-maximisation algorithm Mixture models Parameter estimation Gauss algorithms Univariate

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RECONSTRUCTION FOR STOCHASTIC 3-D SIGNALS WITH SYMMETRIC STATISTICS IN NOISE: ELECTRON MICROSCOPY OF VIRUS PARTICLES

RECONSTRUCTION FOR STOCHASTIC 3-D SIGNALS WITH SYMMETRIC STA...

引用

IEEE International Conference on Image Processing

作者： Nan Xu Peter C. Doerschuk School of Electrical and Computer Engineering Cornell University

ISBN: (纸本)9781479983407

Cryo electron microscopy leads to 3-D image reconstruction problems. The data is one projection of each of many different instances of the object. When the object has symmetry and is also heterogeneous, it is natural to describe the object as stochastic with symmetrical statistics. This paper describes methods for incorporating the symmetry into expectation-maximization algorithms for the maximum likelihood solution of the reconstruction problem.

关键词： Cryo electron microscopy Viruses Symmetrical statistics Maximum likelihood reconstruction Cryoelectron Microscopy viruses Electron Microscope Reconstructions expectation-maximisation algorithm CENTROSYMMETRIC signals Noise

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Robust clustering of data collected via crowdsourcing

Robust clustering of data collected via crowdsourcing

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Alba Pages-Zamora Georgios B. Giannakis Roberto Lopez-Valcarce Pere Gimenez-Febrer SPCOM Group Universitat Politecnica de Catalunya-Barcelona Tech Spain Dept. of ECE and DTC University of Minnesota Minneapolis USA GPSC Universidade de Vigo Spain

ISBN: (纸本)9781509041183

Crowdsourcing approaches rely on the collection of multiple individuals to solve problems that require analysis of large data sets in a timely accurate manner. The inexperience of participants or annotators motivates well robust techniques. Focusing on clustering setups, the data provided by all annotators is suitably modeled here as a mixture of Gaussian components plus a uniformly distributed random variable to capture outliers. The proposed algorithm is based on the expectation-maximization algorithm and allows for soft assignments of data to clusters, to rate annotators according to their performance, and to estimate the number of Gaussian components in the non-Gaussian/Gaussian mixture model, in a jointly manner.

关键词： Crowdsourcing Gaussian plus non-Gaussian Mixture Outlier EM algorithm Bayesian Information Criterion expectation-maximisation algorithm outliers Crowdsource Bayesian information criteria Random variable Cluster Analysis Gauss

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Unambiguity Regularization for Unsupervised Learning of Probabilistic Grammars 12

Unambiguity Regularization for Unsupervised Learning of Prob...

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Conference on Empirical Methods in Natural Language Processing

作者： Kewei Tu Vasant Honavar Departments of Statistics and Computer Science University of California Los Angeles Los Angeles CA 90095 USA Department of Computer Science Iowa State University Ames IA 50011 USA

ISBN: (纸本)9781622765034

We introduce a novel approach named unambiguity regularization for unsupervised learning of probabilistic natural language grammars. The approach is based on the observation that natural language is remarkably unambiguous in the sense that only a tiny portion of the large number of possible parses of a natural language sentence are syntactically valid. We incorporate an inductive bias into grammar learning in favor of grammars that lead to unambiguous parses on natural language sentences. The resulting family of algorithms includes the expectation-maximization algorithm (EM) and its variant, Viterbi EM, as well as a so-called softmax-EM algorithm. The softmax-EM algorithm can be implemented with a simple and computationally efficient extension to standard EM. In our experiments of unsupervised dependency grammar learning, we show that unambiguity regularization is beneficial to learning, and in combination with annealing (of the regularization strength) and sparsity priors it leads to improvement over the current state of the art.

关键词： Unsupervised learning syntax expectation-maximisation algorithm Language natural languages Learning algorithms Probability current status Arts natural

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Probabilistic Low-Rank Matrix Completion with Adaptive Spectral Regularization algorithms 13

Probabilistic Low-Rank Matrix Completion with Adaptive Spect...

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Annual Conference on Neural Information Processing Systems

作者： Adrien Todeschini Francois Caron Marie Chavent INRIA - IMB - Univ. Bordeaux 33405 Talence France Univ. Oxford Dept. of Statistics Oxford OX1 3TG UK Univ. Bordeaux - IMB - INRIA 33000 Bordeaux France

ISBN: (纸本)9781632660244

We propose a novel class of algorithms for low rank matrix completion. Our approach builds on novel penalty functions on the singular values of the low rank matrix. By exploiting a mixture model representation of this penalty, we show that a suitably chosen set of latent variables enables to derive an expectation-Maximization algorithm to obtain a Maximum A Posteriori estimate of the completed low rank matrix. The resulting algorithm is an iterative soft-thresholded algorithm which iteratively adapts the shrinkage coefficients associated to the singular values. The algorithm is simple to implement and can scale to large matrices. We provide numerical comparisons between our approach and recent alternatives showing the interest of the proposed approach for low rank matrix completion.

关键词： rank of matrix algorithms matrices Rank of data Completion contraction coefficient expectation-maximisation algorithm penalty function penalty Singular Probability matrix

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UNDER-DETERMINED CONVOLUTIVE BLIND SOURCE SEPARATION USING SPATIAL COVARIANCE MODELS

UNDER-DETERMINED CONVOLUTIVE BLIND SOURCE SEPARATION USING S...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Ngoc Q.K. Duong Emmanuel Vincent Remi Gribonval METISS project team IRISA-INRIA Campus de Beaulieu 35042 Rennes Cedex France

ISBN: (纸本)9781424442959;9781424442966

This paper deals with the problem of under-determined convolutive blind source separation. We model the contribution of each source to all mixture channels in the time-frequency domain as a zero-mean Gaussian random variable whose covariance encodes the spatial properties of the source. We consider two covariance models and address the estimation of their parameters from the recorded mixture by a suitable initialization scheme followed by an iterative expectation-maximization (EM) procedure in each frequency bin. We then align the order of the estimated sources across all frequency bins based on their estimated directions of arrival (DOA). Experimental results over a stereo reverberant speech mixture show the effectiveness of the proposed approach.

关键词： Convolutive blind source separation under-determined mixtures spatial covariance models EM algorithm permutation problem expectation-maximisation algorithm Covariance Blind source separation Frequency-domain analysis

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Source and Direction of Arrival Estimation Based on Maximum Likelihood Combined with GMM and Eigenanalysis

Source and Direction of Arrival Estimation Based on Maximum ...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： R. Nishimura Y. Suzuki National Institute of Information and Communications Technology Resilient ICT Research Center 2-1-3 Katahira Aoba-ku Sendai Japan Tohoku University Research Institute of Electrical Communication 2-1-1 Katahira Aoba-ku Sendai Japan

ISBN: (纸本)9781538646595

A method is proposed for estimating the source signal and its direction of arrival (DOA) in this paper. It is based on ML estimation of the transfer function between microphones combined with the EM algorithm for a Gaussian Mixture Model (GMM), assuming that the signal is captured at each microphone with delay corresponding to the traveling of sound and some decay. By this modeling, search for the maximum log-likelihood in the ML estimation can be realized simply by eigenvalue decomposition of a properly designed matrix. Computer simulation results show that the proposed method achieves SDR of greater than 10 dB regardless of amplitude difference between microphones and DOA estimation error of less than 8 degrees, on average. It is also shown that it can maintain high performance in various conditions.

关键词： ML estimation Gaussian Mixture Model Rayleigh quotient sparseness time-frequency masking Rayleigh quotient Microphone Device Component Gm(m) Direction of arrival estimation maximum likelihood Phase Mask amplitude difference expectation-maximisation algorithm TRANSFER FUNCTIONS Mixture models Computer Programming Languages SDPR gene

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