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

共 493 条记录，以下是381-390 订阅

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Large-Scale Bayesian Multi-Label Learning via Topic-Based Label Embeddings 15

Large-Scale Bayesian Multi-Label Learning via Topic-Based La...

引用

Annual Conference on Neural Information Processing Systems

作者： Piyush Rai Changwei Hu Ricardo Henao Lawrence Carin CSE Dept IIT KanpurECE Dept Duke University ECE Dept Duke University

ISBN: (纸本)9781510825024

We present a scalable Bayesian multi-label learning model based on learning low-dimensional label embeddings. Our model assumes that each label vector is generated as a weighted combination of a set of topics (each topic being a distribution over labels), where the combination weights (i.e., the embeddings) for each label vector are conditioned on the observed feature vector. This construction, coupled with a Bernoulli-Poisson link function for each label of the binary label vector, leads to a model with a computational cost that scales in the number of positive labels in the label matrix. This makes the model particularly appealing for real-world multi-label learning problems where the label matrix is usually very massive but highly sparse. Using a data-augmentation strategy leads to full local conjugacy in our model, facilitating simple and very efficient Gibbs sampling, as well as an expectation Maximization algorithm for inference. Also, predicting the label vector at test time does not require doing an inference for the label embeddings and can be done in closed form. We report results on several benchmark data sets, comparing our model with various state-of-the art methods.

关键词： expectation-maximisation algorithm Bayesian inference Dataset TESTING TIME Eigenvector closed forms

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Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables

Exact Adversarial Attack to Image Captioning via Structured ...

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yan Xu Baoyuan Wu Fumin Shen Yanbo Fan Yong Zhang Heng Tao Shen Wei Liu University of Electronic Science and Technology of China Tencent AI Lab

ISBN: (纸本)9781728132945

In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate some targeted partial captions for an image polluted by adversarial noises, even the targeted captions are totally irrelevant to the image content. A partial caption indicates that the words at some locations in this caption are observed, while words at other locations are not restricted. It is the first work to study exact adversarial attacks of targeted partial captions. Due to the sequential dependencies among words in a caption, we formulate the generation of adversarial noises for targeted partial captions as a structured output learning problem with latent variables. Both the generalized expectation maximization algorithm and structural SVMs with latent variables are then adopted to optimize the problem. The proposed methods generate very successful attacks to three popular CNN+RNN based image captioning models. Furth ermore, the proposed attack methods are used to understand the inner mechanism of image captioning systems, providing the guidance to further improve automatic image captioning systems towards human captioning.

关键词： captions Imagery (Psychotherapy) images Word Image expectation-maximisation algorithm

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LOCAL LIKELIHOOD ESTIMATION OF TIME-VARIANT HAWKES MODELS

LOCAL LIKELIHOOD ESTIMATION OF TIME-VARIANT HAWKES MODELS

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Boris I. Godoy Victor Solo Jason Min Syed Ahmed Pasha School of Electrical Eng. & Telecommunications The University of New South Wales Sydney Australia Department of Electrical Engineering Air University Pakistan

ISBN: (纸本)9781479999897

The Hawkes process is the workhorse of dynamic point process modelling - the point process version of the autoregression. It has been applied, for example, in high frequency finance, electricity price spike modelling and gene regulatory network modelling. But, in all these and other applications, it is assumed the parameters are time invariant. However, it is becoming clear that in many applications the parameters vary with time. Here, we develop for the first time, a very simple local likelihood approach to estimation of time-variant Hawkes processes. The new algorithm is tested on simulations and then applied to data from the Australian electricity market.

关键词： Point processes time-variant parameters system identification local likelihood EM algorithm expectation-maximisation algorithm point operation System identification time varying power markets autoregressive processes Modeling

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Empirical Bayes estimation utilizing finite Gaussian Mixture Models

Empirical Bayes estimation utilizing finite Gaussian Mixture...

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CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

作者： Rafael Orellana Rodrigo Carvajal Juan C. Agüero Universidad Técnica Federico Santa María Chile

ISBN: (纸本)9781728131863

In this paper we develop an identification algorithm to obtain an estimation of the prior distribution in the classical problem of Bayesian inference. We consider the Empirical Bayes approach to obtain the prior distribution approximation by a finite Gaussian mixture. An expectation-Maximization based algorithm is used to obtain an estimate of the Gaussian mixture parameters. Our approach shows a good approximation of the prior distribution when the number of experiments is increased. We illustrate the estimation performance of our proposal with numerical simulations.

关键词： Bayes methods expectation-maximisation algorithm Gaussian processes expectation-maximisation algorithm Bayes methods Gaussian processes Prior distribution Bayesian inference Bayes

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Outlier Detection algorithm Based on Gaussian Mixture Model

Outlier Detection Algorithm Based on Gaussian Mixture Model

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IEEE International Conference on Power, Intelligent Computing and Systems

作者： Wenbo Liu Delong Cui Zhiping Peng Jihai Zhong Faulty of Computer Science Guangdong University of Technology Department of Computer and Electronic Information Guangdong University of Petrochemical Technology

ISBN: (纸本)9781728137216

Outlier detection is an important aspect in the field of data mining. In order to solve the problem of outlier detection in high-dimensional datasets, an outlier detection algorithm based on Gaussian mixture model is proposed. First of all, for the data set to be tested, the global optimization expectation maximization algorithm is used to fit a Gaussian mixture model, and then the three-time standard deviation principle is introduced on each Gaussian component, the outlier is the data point outside the range of the mean deviation of the mean value of three times the standard deviation. Through the experiments on the simulation dataset and the real data set, the effectiveness of the algorithm on the outlier detection of high-dimensional data sets is verified.

关键词： Data mining Outlier detection Gaussian mixture model expectation maximization algorithm The principle of three times standard deviation expectation-maximisation algorithm data mining Mixture models outliers Detection algorithms Dataset Mean deviation mean value

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PEAK MODELING FOR ION MOBILITY SPECTROMETRY MEASUREMENTS

PEAK MODELING FOR ION MOBILITY SPECTROMETRY MEASUREMENTS

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European Signal Processing Conference

作者： Dominik Kopczynski Joerg Ingo Baumbach Sven Rahmann Bioinformatics for High-Throughput Technologies Computer Science XI Department Microfluidics and Clinical Diagnostics KIST Europe Genome Informatics Institute of Human Genetics Faculty of Medicine University of Duisburg-Essen

ISBN: (纸本)9781467310680

Ion mobility spectrometry (IMS), coupled with multicapillary columns (MCCs), is a technology for analyzing the concentration of volatile organic compounds (VOCs) in the air or in exhaled breath. We introduce a new model-based method for peak description and thus for manifold data reduction. Depending on the number of peaks, we reduce the raw data by five orders of magnitude (about 250 000-fold). Each peak is described with seven parameters, which are estimated by an expectation maximization (EM) algorithm that copes well with overlapping peaks. We transform the parameters into robust and interpretable peak descriptors, which are suitable for downstream analysis steps.

关键词： Ion mobility spectrometry (IMS) Data reduction Parameter estimation Mixture model EM algorithm Three-parameter inverse Gaussian distribution Spectrometry peaks Modeling Volatile Organic Compounds ion mobility spectrometry expectation-maximisation algorithm EARTHQUAKE MAGNITUDE RAW DATA WWOX gene air

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AUDIO-VISUAL EMOTION RECOGNITION WITH BOOSTED COUPLED HMM

AUDIO-VISUAL EMOTION RECOGNITION WITH BOOSTED COUPLED HMM

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International Conference on Pattern Recognition

作者： Kun LU Yunde JIA School of Software Beijing Lab of Intelligent Information Tech School of Computer Science Beijing Institute of Technology

ISBN: (纸本)9781467322164

This paper presents a novel approach for automatic audio-visual emotion recognition. The audio and visual channels provide complementary information for human emotional states recognition, and we utilize coupled HMM (CHMM) as model-level fusion method in our work. To further improve recognition accuracy, we design an AdaBoost-CHMM ensemble classifier which takes CHMM as component classifiers in adaptive boosting procedure. A modified expectation-maximization (EM) algorithm for CHMM learning is proposed to make the learning, process focus more on difficult samples. Experiment results on audio-visual emotion data collected in Wizard of Oz scenarios and labeled under two types of emotion category sets demonstrate that our approach is effective and promising.

关键词： emotion recognition expectation-maximisation algorithm hidden Markov models image classification learning (artificial intelligence)

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A concave regularization technique for sparse mixture models 11

A concave regularization technique for sparse mixture models

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Annual Conference on Neural Information Processing Systems

作者： Martin Larsson Johan Ugander School of Operations Research and Information Engineering Cornell University Center for Applied Mathematics Cornell University

ISBN: (纸本)9781618395993

Latent variable mixture models are a powerful tool for exploring the structure in large datasets. A common challenge for interpreting such models is a desire to impose sparsity, the natural assumption that each data point only contains few latent features. Since mixture distributions are constrained in their L_1 norm, typical sparsity techniques based on L_1 regularization become toothless, and concave regularization becomes necessary. Unfortunately concave regularization typically results in EM algorithms that must perform problematic non-concave M-step maximizations. In this work, we introduce a technique for circumventing this difficulty, using the so-called Mountain Pass Theorem to provide easily verifiable conditions under which the M-step is well-behaved despite the lacking concavity. We also develop a correspondence between logarithmic regularization and what we term the pseudo-Dirichlet distribution, a generalization of the ordinary Dirichlet distribution well-suited for inducing sparsity. We demonstrate our approach on a text corpus, inferring a sparse topic mixture model for 2,406 weblogs.

关键词： CONCAVE Theorem blogs Weblogs Mixture models Mountain roads expectation-maximisation algorithm Correspondence

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AN expectation-MAXIMIZATION METHOD FOR THE PERMUTATION PROBLEM IN FREQUENCY-DOMAIN BLIND SOURCE SEPARATION

AN EXPECTATION-MAXIMIZATION METHOD FOR THE PERMUTATION PROBL...

引用

IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Thom-Thi Ngo Seung-Hyon Nam Department of Electronic Engineering Paichai University 439-6 Doma-dong Seo-gu Daejeon Korea

ISBN: (纸本)9781424442959;9781424442966

This paper describes a new method for solving the permutation problem in frequency-domain blind source separation (FDBSS). The permutation problem in FDBSS is the well-known order-changing problem of separating spectra, which is caused by the independent separating process in each frequency bin. The new solution is based on the expectation-maximization (EM) technique applied to phases of inverse separating filters. The EM algorithm treats spatial aliasing implicitly and exploits statistics of a room environment embedded in the inverse of separating filters. As a result, it provides more robust performance than other existing methods based on source directions. In addition, it does not require any knowledge of the microphone geometry. Experimental results in reverberant environments demonstrate good performance of the proposed method.

关键词： Permutation solution frequency-domain blind source separation expectation-Maximization expectation-maximisation algorithm Frequency-domain analysis Robust control Inverse new methods Permutation indium Filters

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Vectorcardiographic loop alignment for fetal movement detection using the expectation-Maximization algorithm and Support Vector Machines

Vectorcardiographic loop alignment for fetal movement detect...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： R. Vullings M. Mischi Faculty of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (纸本)9781457702150

Reduced fetal movement is an important parameter to assess fetal distress. Currently, no suitable methods are available that can objectively assess fetal movement during pregnancy. Fetal vectorcardiographic (VCG) loop alignment could be such a method. In general, the goal of VCG loop alignment is to correct for motion-induced changes in the VCGs of (multiple) consecutive heartbeats. However, the parameters used for loop alignment also provide information to assess fetal movement. Unfortunately, current methods for VCG loop alignment are not robust against low-quality VCG signals. In this paper, a more robust method for VCG loop alignment is developed that includes a priori information on the loop alignment, yielding a maximum a posteriori loop alignment. Classification, based on movement parameters extracted from the alignment, is subsequently performed using support vector machines, resulting in correct classification of (absence of) fetal movement in about 75% of cases. After additional validation and optimization, this method can possibly be employed for continuous fetal movement monitoring.

关键词： Fetal Movement Alignment Support Vector Network expectation-maximisation algorithm Moving parameter

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