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检索条件"主题词=Expectation Maximization Algorithm"

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Learning Staged Trees from Incomplete Data 12

Learning Staged Trees from Incomplete Data

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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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DeepCensored: Deep-Learning Based Probabilistic Forecasting Framework for Censored Data 23

DeepCensored: Deep-Learning Based Probabilistic Forecasting ...

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

作者： Tian, Jiahao Porter, Michael D. School of Data Science University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia Dept. of Systems and Information Engineering CharlottesvilleVA United States

ISBN: (纸本)9798350374889

Accurate time series forecasting is critical in various fields, including resource allocation and crime prevention. While traditional approaches often focus on continuous data, count data forecasting, especially with partially observed (censored) data, remains challenging. This paper introduces DeepCensored, a novel deep learning-based framework that combines the expectation-maximization (EM) algorithm with deep neural networks to deliver robust probabilistic forecasts. DeepCensored naturally handles interval-censored event data, where exact event times are unknown but fall within a specific interval. Through extensive simulations and real-world crime data analysis, our method significantly outperforms traditional forecasting approaches, reducing the Mean Absolute Error (MAE) by 50% and better detection of emerging crime trends. These results highlight DeepCensored's potential to provide actionable insights for law enforcement and public safety by predicting crime intensity and enabling resource-efficient policing strategies. © 2024 IEEE.

关键词： expectation maximization algorithm

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Graph Neural Networks with a Distribution of Parametrized Graphs 41

Graph Neural Networks with a Distribution of Parametrized Gr...

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41st International Conference on Machine Learning, ICML 2024

作者： Lee, See Hian Ji, Feng Xia, Kelin Tay, Wee Peng School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Physical and Mathematical Sciences Nanyang Technological University Singapore

Traditionally, graph neural networks have been trained using a single observed graph. However, the observed graph represents only one possible realization. In many applications, the graph may encounter uncertainties, such as having erroneous or missing edges, as well as edge weights that provide little informative value. To address these challenges and capture additional information previously absent in the observed graph, we introduce latent variables to parameterize and generate multiple graphs. The parameters follow an unknown distribution to be estimated. We propose a formulation in terms of maximum likelihood estimation of the network parameters. Therefore, it is possible to devise an algorithm based on expectation-maximization (EM). Specifically, we iteratively determine the distribution of the graphs using a Markov Chain Monte Carlo (MCMC) method, incorporating the principles of PAC-Bayesian theory. Numerical experiments demonstrate improvements in performance against baseline models on node classification for both heterogeneous and homogeneous graphs. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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How to Design a R-D Model for UAV Video Transmission? 15

How to Design a R-D Model for UAV Video Transmission?

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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Yu, Zongyang Yang, Peng Cao, Xianbin Zheng, Dezhi Quek, Tony Q. S. Oliver Wu, Dapeng Beihang University School of Electronic Information Engineering Beijing China Peng Cheng Laboratory Department of Mathematics and Theories Shenzhen China Beijing Institute of Technology Advanced Research Institute of Multidisciplinary Sciences Beijing China Singapore University of Technology and Design Information Systems Technology and Design Singapore City University of Hong Kong Department of Computer Science Kowloon Hong Kong

ISBN: (纸本)9798350324662

Unmanned aerial vehicle (UAV) video transmission has been extensively applied in many crucial fields. However, the problem of designing a rate-distortion (R-D) model for UAV video transmission, which is essential for theoretical analysis of video coding and optimization of video transmission quality, is under-studied. The designed R-D model is desired to be simple, accurate, and generic owing to the limited capability of the UAV. Observing the key role of transformed residuals in the R-D model, this paper elaborately discusses the modeling of the transformed residual distribution and proposes an estimation algorithm to estimate the statistical parameter of the distribution. Specifically, considering the stringent requirements for low complexity and high generalization capability, we first design a linear parameter estimator by mining and analyzing UAV video statistical characteristics. Further, we develop a bias update scheme to improve the accuracy of the estimator. Test results on multiple real video sequences taken by UAVs show that the proposed estimation algorithm is more accurate than benchmarks. © 2023 IEEE.

关键词： expectation maximization algorithm R-D model rate control video coding Video transmission

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Centralized Selection with Preferences in the Presence of Biases 41

Centralized Selection with Preferences in the Presence of Bi...

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41st International Conference on Machine Learning, ICML 2024

作者： Elisa Celis, L. Kumar, Amit Vishnoi, Nisheeth K. Xu, Andrew Yale University United States IIT Delhi India

This paper considers the scenario in which there are multiple institutions, each with a limited capacity for candidates, and candidates, each with preferences over the institutions. A central entity evaluates the utility of each candidate to the institutions, and the goal is to select candidates for each institution in a way that maximizes utility while also considering the candidates' preferences. The paper focuses on the setting in which candidates are divided into multiple groups and the observed utilities of candidates in some groups are biased-systematically lower than their true utilities. The first result is that, in these biased settings, prior algorithms can lead to selections with sub-optimal true utility and significant discrepancies in the fraction of candidates from each group that get their preferred choices. Subsequently, an algorithm is presented along with proof that it produces selections that achieve near-optimal group fairness with respect to preferences while also nearly maximizing the true utility under distributional assumptions. Further, extensive empirical validation of these results in real-world and synthetic settings, in which the distributional assumptions may not hold, are presented. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Parameter Estimation in DAGs from Incomplete Data via Optimal Transport 41

Parameter Estimation in DAGs from Incomplete Data via Optima...

引用

41st International Conference on Machine Learning, ICML 2024

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

Estimating the parameters of a probabilistic directed graphical model from incomplete data is a long-standing challenge. This is because, in the presence of latent variables, both the likelihood function and posterior distribution are intractable without assumptions about structural dependencies or model classes. While existing learning methods are fundamentally based on likelihood maximization, here we offer a new view of the parameter learning problem through the lens of optimal transport. This perspective licenses a general framework that operates on any directed graphs without making unrealistic assumptions on the posterior over the latent variables or resorting to variational approximations. We develop a theoretical framework and support it with extensive empirical evidence demonstrating the versatility and robustness of our approach. Across experiments, we show that not only can our method effectively recover the ground-truth parameters but it also performs comparably or better than competing baselines on downstream applications. Copyright 2024 by the author(s)

关键词： expectation maximization algorithm

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Safety Analysis of China's Strategic Material Maritime Transport Channel Based on Bayesian Network 14

Safety Analysis of China's Strategic Material Maritime Trans...

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14th International Conference on Information Science and Technology, ICIST 2024

作者： Chen, Xue Wang, Xin Yu, Qing Feng, Lingling Zheng, Yuping College of Navigation Dalian Maritime University Liaoning China College of Navigation Jimei Maritime University Fujian China China Institute of Marine Technology and Economy Beijing China China Waterborne Transport Research Institute Beijing China

ISBN: (纸本)9798350353334

Aiming at the influence of the maritime transport channel environment on the safety of maritime transport of strategic materials in China, a safety environment assessment model of the maritime transport channel based on the Bayesian network is proposed. First of all, fully considering the environmental impact factors of traditional maritime transport channels, and combining them with non-traditional factors such as shipping events and sudden public health events, an index system of environmental impact factors of maritime transport channels for strategic materials in China is constructed. Based on expert experience and historical data, a model of environmental factors for marine accident risk at key nodes of maritime transportation channels based on the Bayesian Network (BN) was proposed. At the same time, using the actual data of the key nodes of China's strategic materials maritime transport channel from 2012 to 2022, such as maritime casualties and other related environmental factors, the expectation maximization (EM) algorithm is used to compensate for the absence of data and adaptively learn the parameters to update the parameters of the conditional probability table. Finally, the sensitivity analysis of the environmental factors affecting the safety of sea lanes is carried out. It is verified that the BN model can effectively identify the environmental factors of sea transport channels, and can accurately quantify the importance of environmental factors to the safety of sea transport of strategic materials in China. The analysis results show that the channel environment is the main factor affecting the safety of China's strategic materials maritime transport channels. © 2024 IEEE.

关键词： expectation maximization algorithm

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Energy Efficiency in 5G Networks Using Gaussian Mixture Models

Energy Efficiency in 5G Networks Using Gaussian Mixture Mode...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Alsalami, Zaid Kasthuri, R. Sowmiya, S. Rani, Pooja Gupta, Deepak Kumar Gowtham Kumar Reddy, I.V. The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Sri Ramakrishna Engineering College Department of Mathematics Tamilnadu Coimbatore India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Science Engineering Punjab Mohali140307 India Ies College of Technology Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha Institute of Medical and Technical Sciences Department of CSE-AI Tamilnadu Chennai India

ISBN: (纸本)9798350390759

In this research, a new approach for improving energy efficiency of the networks in the 5G context is presented: utilizing the Gaussian Mixture Models. The process includes acquiring data in the first step, training the model in the second step, and using the model to make real-time decisions to minimize resources and inefficient energy consumption in the last step. This includes the collection of data on context and complete flow details of the course of traffic passage as well as actions committed by users. The GMM is then trained using the expectation-maximization (EM) algorithm for developing the model and reinforcement learning for real-time decision-making on the developed model. In case studies analyzed, there is evidence of the enhancement of energy consumption by 14% on average absolute reduction in energy per traffic unit, by 11% of the average absolute increase in network throughput and by 49% of the mean absolute reduction of latency in comparison with baseline conditions. © 2024 IEEE.

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