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检索条件"主题词=Expectation Maximization Algorithm"

共 651 条记录，以下是421-430 订阅

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Robust EM algorithms for Markov modulated Poisson processes

Robust EM algorithms for Markov modulated Poisson processes

引用

39th IEEE Conference on Decision and Control

作者： Elliott, RJ Malcolm, WP Univ Alberta Dept Math Sci Edmonton AB T6G 21 Canada

ISBN: (纸本)0780366387

In this article we consider robust filtering and smoothing for Markov Modulated Poisson Processes (MMPPs). Using the EM algorithm, these filters and smoothers can be applied to estimate the parameters of our model. Our dynamics do not involve stochastic integrals and our new formulae, in terms of time integrals, are easily discretized.

关键词： expectation maximization algorithm counting processes change of measure martingales

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Optimal Transport for Structure Learning Under Missing Data 41

Optimal Transport for Structure Learning Under Missing Data

引用

41st International Conference on Machine Learning, ICML 2024

作者： Vo, Vy Zhao, He Le, Trung Bonilla, Edwin V. Phung, Dinh Monash University Australia CSIRO's Data61 Australia VinAI Research Viet Nam

Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma. While the goal is to recover the true causal structure, robust imputation requires considering the dependencies or, preferably, causal relations among variables. Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirically shown to be sub-optimal. To address this problem, we propose a score-based algorithm for learning causal structures from missing data based on optimal transport. This optimal transport viewpoint diverges from existing score-based approaches that are dominantly based on expectation maximization. We formulate structure learning as a density fitting problem, where the goal is to find the causal model that induces a distribution of minimum Wasserstein distance with the observed data distribution. Our framework is shown to recover the true causal graphs more effectively than competing methods in most simulations and real-data settings. Empirical evidence also shows the superior scalability of our approach, along with the flexibility to incorporate any off-the-shelf causal discovery methods for complete data. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Deep SBL Network-Based DOA Estimation for Nested Array with Mutual Coupling

Deep SBL Network-Based DOA Estimation for Nested Array with ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Su, Xiaolong Gong, Zhenghui Hu, Panhe Liu, Tianpeng Liu, Zhen College of Electronic Science and Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781839539954

Recently, deep unfolding networks have been widely used in direction of arrival (DOA) estimation because of the improved estimation accuracy and reduced computational cost. However, few consider the existence of nested array (NA) with mutual coupling. In this paper, we present a deep sparse Bayesian learning (DSBL) network to solve this problem. We first transform the array output with unknown mutual coupling coefficient (MCC) into real domain for neural networks. We then train the DSBL network to determine the MCC and obtain DOA spatial spectrum, where the loss function is calculated by using reconstruction error and the sparsity of network output, and the layers correspond to the steps of expectation maximization (EM) algorithm. We demonstrate that DSBL network can achieve better generalization ability without training labels and large-scale training data. Simulation results validate the effectiveness of DSBL network by comparing with the existing methods. © The Institution of Engineering & Technology 2023.

关键词： expectation maximization algorithm

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An expectation-Realization Model for Metaphor Detection 4

An Expectation-Realization Model for Metaphor Detection

引用

4th Workshop on Figurative Language Processing, FigLang 2024

作者： Uduehi, Oseremen O. Bunescu, Razvan C. School of EECS Ohio University AthensOH45701 United States Department of Computer Science University of North Carolina at Charlotte CharlotteNC28223 United States

ISBN: (纸本)9798891761100

We propose a new model for metaphor detection in which an expectation component estimates representations of expected word meanings in a given context, whereas a realization component computes representations of target word meanings in context. We also introduce a systematic evaluation methodology that estimates generalization performance in three settings: within distribution, a new strong out of distribution setting, and a novel out-of-pretraining setting. Across all settings, the expectation-realization model obtains results that are competitive with or better than previous metaphor detection models. © 2024 Association for Computational Linguistics.

关键词： expectation maximization algorithm

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Learning Input Driven Dynamic Bayesian Networks with Measurement Noise 23

Learning Input Driven Dynamic Bayesian Networks with Measure...

引用

23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Veres, David Li, Ping Kadirkamanathan, Visakan The University of Sheffield Department of Automatic Control and Systems Engineering Sheffield United Kingdom

ISBN: (纸本)9798350374889

Dynamic Bayesian Networks (DBNs) are useful tools for modelling complex systems whose network representations can be elicited a priori or learnt from data. In this paper, a maximum likelihood Doubly-Iterative expectation maximization (DI-EM) algorithm is developed for the identification of grey-box ARMAX state-space model representations of DBNs involving known, noisy measurement processes. The grey-box model incorporates network dependencies among time series variables and exploits time series data of low longitudinal and high cross-sectional dimensions. A network learning procedure is developed using a score-based structure-selection method to find the underlying network of an input-driven dynamical system. By computing a finite data version of the Bayesian Information Criterion (BIC) for small sample sizes, the proposed method's performance is investigated on simulated and real-world data. The algorithm recovers the underlying ground-truth networks of simulated systems under finite data criteria with Jaccard Coefficient values of up to 0.84, and selects structures with improved weighted mean-squared error loss over a baseline black-box model fit on real-world data. © 2024 IEEE.

关键词： expectation maximization algorithm

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The Forward-Reverse Greedy algorithm for Distributed Submodular maximization 63

The Forward-Reverse Greedy Algorithm for Distributed Submodu...

引用

63rd IEEE Conference on Decision and Control, CDC 2024

作者： Tackett, Justin Grimsman, David Brigham Young University Provo Computer Science Department UT United States

ISBN: (纸本)9798350316339

This work addresses submodular maximization problems, a widely-used mathematical tool to model many real-world decisions. Though this set of problems is NP-Hard, a well-known result is that a distributed greedy algorithm, wherein agents sequentially make greedy decisions, is guaranteed to approximate the optimal solution with a multiplicative factor of 1/2. This work explores whether running the greedy algorithm with different agent orderings can provide stronger guarantees. In particular, we show that for two-agent systems, running the distributed greedy algorithm with both possible sequences ensures a guarantee of 2/3. We also show that this method does not benefit systems of more than two agents, but opens the door to future work for exploring other ways to sequence these systems. © 2024 IEEE.

关键词： expectation maximization algorithm

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Multiple Target Detection in Radar Systems: A Hybrid Approach Combining EM Clustering and Sparsity-based Reconstruction

Multiple Target Detection in Radar Systems: A Hybrid Approac...

引用

2024 IEEE International Workshop on Technologies for Defense and Security, TechDefense 2024

作者： Sun, Jiarui Hao, Chengpeng Yan, Linjie Orlando, Danilo Institute of Acoustics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Università di Pisa Dipartimento di Ingegneria dell'Informazione Pisa Italy

ISBN: (纸本)9798331505585

This paper addresses the challenge of detecting multiple targets embedded in Gaussian noise with unknown range positions and angles of arrival. To this end, we introduce the signal classification model of interest resorting to the latent variable model. Meanwhile, an estimation procedure is proposed, combining the expectation-maximization algorithm with sparsity-based reconstruction method. The expectationmaximization algorithm classifies targets using the maximum a posteriori criterion, while the sparsity-based reconstruction method estimates the angles of arrival of targets utilizing sparsitypromoting prior. The estimated parameters are then applied to a decision scheme based on the Likelihood Ratio Test. Finally, numerical examples illustrate the effectiveness of the proposed method for classification and detection. © 2024 IEEE.

关键词： expectation maximization algorithm

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Learning Staged Trees from Incomplete Data 12

Learning Staged Trees from Incomplete Data

引用

12th International Conference on Probabilistic Graphical Models, PGM 2024

作者： Carter, Jack Storror Leonelli, Manuele Riccomagno, Eva Varando, Gherardo Dipartimento di Matematica Università degli Studi di Genova Genova Italy School of Science and Technology IE University Madrid Spain Image Processing Laboratory Universitat de València València Spain

Staged trees are probabilistic graphical models capable of representing any class of non-symmetric independence via a coloring of their vertices. Several structural learning routines have been defined and implemented to learn staged trees from data, under the frequentist or Bayesian paradigm. They assume a data set has been observed fully and, in practice, observations with missing entries are either dropped or imputed before learning the model. Here, we introduce the first algorithms for staged trees that handle missingness within the learning of the model. To this end, we characterize the likelihood of staged tree models in the presence of missing data and discuss pseudo-likelihoods that approximate it. A structural expectation-maximization algorithm estimating the model directly from the full likelihood is also implemented and evaluated. A computational experiment showcases the performance of the novel learning algorithms, demonstrating that it is feasible to account for different missingness patterns when learning staged trees. © 2024 J.S. Carter, M. Leonelli, E. Riccomagno & G. Varando.

关键词： expectation maximization algorithm

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SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning 41

SimPro: A Simple Probabilistic Framework Towards Realistic L...

引用

41st International Conference on Machine Learning, ICML 2024

作者： Du, Chaoqun Han, Yizeng Huang, Gao Department of Automation BNRist Tsinghua University Beijing China

Recent advancements in semi-supervised learning have focused on a more realistic yet challenging task: addressing imbalances in labeled data while the class distribution of unlabeled data remains both unknown and potentially mismatched. Current approaches in this sphere often presuppose rigid assumptions regarding the class distribution of unlabeled data, thereby limiting the adaptability of models to only certain distribution ranges. In this study, we propose a novel approach, introducing a highly adaptable framework, designated as SimPro, which does not rely on any predefined assumptions about the distribution of unlabeled data. Our framework, grounded in a probabilistic model, innovatively refines the expectation-maximization (EM) algorithm by explicitly decoupling the modeling of conditional and marginal class distributions. This separation facilitates a closed-form solution for class distribution estimation during the maximization phase, leading to the formulation of a Bayes classifier. The Bayes classifier, in turn, enhances the quality of pseudo-labels in the expectation phase. Remarkably, the SimPro framework not only comes with theoretical guarantees but also is straightforward to implement. Moreover, we introduce two novel class distributions broadening the scope of the evaluation. Our method showcases consistent state-of-the-art performance across diverse benchmarks and data distribution scenarios. Our code is available at https://***/LeapLabTHU/SimPro. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Lunar Surface Target Detection Based on Improved YOLOv8 5

Lunar Surface Target Detection Based on Improved YOLOv8

引用

5th International Conference on Intelligent Computing and Human-Computer Interaction, ICHCI 2024

作者： Yang, Yu Wang, Zhen-Wei Zhao, Han Zhang, Zi-Yu School of Astronautics and Aeronautics University of Electronic Science & Technology of China Chengdu611731 China

ISBN: (纸本)9798350368284

In lunar exploration and research, the accurate identification and localization of lunar surface features is of great scientific significance and application value. However, target detection in lunar surface images faces a series of difficulties. Firstly, the lunar surface rock and soil is relatively monotonous and lacks texture information. Secondly,amount of image data on the lunar surface is less compared to most of the image data and there are overlapping areas between the images. Therefore, manually labeling lunar surface features is susceptible to significant errors. Thirdly, the size of the target in the lunar surface images is small and the resolution of the panorama spliced images of the lunar surface is high, leading to small target detection on high-resolution images a major technical difficulty. Aiming at the difficulty of data annotation of lunar surface images, this paper proposes a data annotation method based on panoramic spliced images. Then an improved YOLOv8 target detection algorithm is proposed by introducing an EMA(expectation-maximization-Attention) attention mechanisms, an adaptive feature fusion module and a sliding window based slice detection strategy, which solves the difficulty of detecting small targets on high-resolution images and avoids the problems of false detection and missed detection. © 2024 IEEE.

关键词： expectation maximization algorithm

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