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3rd IEEE International Conference on Big Data Computing Service and Applications (BigDataService)

作者： Wang, Li-e Guo, Yameng Li, Xianxian Guangxi Normal Univ Coll Comp Sci & Informat Technol Guilin Peoples R China

ISBN: (纸本)9781509063185

Privacy preserving of multi-sensitive attributes datasets (MSA-Datasets) has received increasing attention because of its huge social and economic benefits. In this paper, we introduce a novel and general privacy framework called Transparent Link. The Transparent Link framework can be used to anonymize MSA-Datasets by designing an algorithm based on probabilistic graphical model, which is referred to as APGM. Under the framework, to privately protect the relationships among multiple sensitive attributes, we present a clustering approach which can improve the utility of association rules through probabilistic edge association based on multipartite graphs. Experimental results show that our approach offer strong tradeoffs between privacy and utility.

关键词： Multi-sensitive Privacy Multipartite Graph probabilistic graphical model

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A Community Detection Algorithm Based on probabilistic graphical model 6

A Community Detection Algorithm Based on Probabilistic Graph...

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Shao, Wenbin Nan, Tian Zhang, Zhaozhi Lanzhou University School of Information Science and Engineering Lanzhou China State Grid Gansu Electric Power Company Material Company Lanzhou China

ISBN: (纸本)9798350305623

Many community detection algorithms suffer from problems such as low accuracy and efficiency, overfitting or underfitting, and high time complexity. To address the issues in global optimization algorithms proposed so far, GOMK-BP, a community detection algorithm based on the probabilistic graph model, is proposed in this paper. It takes the fully connected Markov random field (MRF) as the probabilistic graph model, maps the node's similarity and importance indexes to the energy function, which is then optimized to improve the performance of the model, and the combination of weighted sum and exponential function makes the algorithm achieve a better fit. To improve the efficiency and accuracy, the traditional message propagation algorithm is improved, and the convergence is accelerated by using compressed sensing. Because of the fully connected MRF may be affected by the high computational complexity, GOMK-BP adjusts the model structure, and introduces sparse connections to balance the computational efficiency and model performance. Finally, the community structure is obtained. The proposed algorithm is tested on some artificial networks and real networks, the results show that GOMK-BP can detect high-quality community structures. © 2023 IEEE.

关键词： Belief propagation Community detection Markov random field probabilistic graphical model

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PARAMETER ESTIMATION OF NONLINEAR OUTPUT ERROR SYSTEM UNDER VARIATIONAL BAYESIAN METHOD BASED ON probabilistic graphical model

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FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY 2022年第2期30卷 2240075-2240075页

作者： Du, Yiping Katib, Iyad Luliang Univ Dept Math Luliang Shanxi Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Comp Sci Dept Jeddah Saudi Arabia

To estimate the parameters of the nonlinear output error system (nonlinear system), a variational Bayesian estimation method (VB method) is proposed based on the probabilistic graphical model (PGM). First, the related theories are introduced in this study such as the PGM and nonlinear systems. Then, the parameter estimation model of the nonlinear system is established. Finally, a VB method is proposed based on the PGM to estimate the parameters of the nonlinear system, which is tested and verified by numerical simulation experiments. It is found that the parameter estimation model of nonlinear system and the proposed method can estimate the parameters of relevant nonlinear system better, and the minimum error between the parameter estimated value and the actual value is only about 0.0001. It proves the feasibility of the VB method based on PGM in the identification of nonlinear systems. The results of this study provide an important reference for the control and identification of nonlinear systems.

关键词： probabilistic graphical model Variational Bayesian Nonlinear Systems Parameter Estimation

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VIDEO MULTI-TARGET TRACKING BASED ON probabilistic graphical model

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Journal of Electronics(China) 2011年第Z1期 548-557页

作者： Xu Feng Huang Chenrong* Wu Zhengjun Xu Lizhong(College of Computer and Information Engineering,Hohai University,Nanjing 210098,China) *(School of Computer Engineering,Nanjing Institute of Technology,Nanjing 211167,China)

In the technique of video multi-target tracking,the common particle filter can not deal well with uncertain relations among multiple *** solve this problem,many researchers use data association method to reduce the multi-target ***,the traditional data association method is difficult to track accurately when the target is *** remove the occlusion in the video,combined with the theory of data association,this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter ***-perimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm.

关键词： Video tracking Multi-target tracking Data association probabilistic graphical model Particle filter

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VIDEO MULTI-TARGET TRACKING BASED ON probabilistic graphical model

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Journal of Electronics(China) 2011年第4期28卷 548-557页

作者： Xu Feng Huang Chenrong Wu Zhengjun Xu Lizhong College of Computer and Information Engineering Hohai University Nanjing 210098 China School of Computer Engineering Nanjing Institute of Technology Nanjing 211167 China

关键词： Video tracking Multi-target tracking Data association probabilistic graphical model Particle filter

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Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes

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COMPUTERS & CHEMICAL ENGINEERING 2014年 71卷 171-209页

作者： Mori, Junichi Mahalec, Vladimir Yu, Jie McMaster Univ Dept Chem Engn Hamilton ON L8S 4L7 Canada

Identification of faults in process systems can be based purely on measurement (e.g. PCA), or can exploit knowledge of process model structure to construct a causal network. This work introduces a method to identify most likely causal network in cases when process model is not known. An incidence matrix, showing location of measurements in the plant network structure, and historical process data are used to identify the optimal causal network structure by means of maximizing Bayesian scores for alternative causal networks. Causal subnetworks, corresponding to subgraphs of the process network, are identified by finding the most probable graph based on highest posterior probability of graph features computed via Markov Chain Monte Carlo simulation. Novel Bayesian contribution indices within the probabilistic graphical network are proposed to identify the potential root-cause variables. Application to Tennessee Eastman Chemical plant demonstrates that the presented method is significantly more accurate than the current methods. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： probabilistic graphical model Cause-effect relationship Structure learning Incidence matrix Markov chain Monte Carlo simulation Root-cause diagnosis

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Video multi-target tracking based on probabilistic graphical model

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Journal of Electronics 2011年第4期28卷 548-557页

作者： Xu, Feng Huang, Chenrong Wu, Zhengjun Xu, Lizhong College of Computer and Information Engineering Hohai University Nanjing 210098 China School of Computer Engineering Nanjing Institute of Technology Nanjing 211167 China

In the technique of video multi-target tracking, the common particle filter can not deal well with uncertain relations among multiple targets. To solve this problem, many researchers use data association method to reduce the multi-target uncertainty. However, the traditional data association method is difficult to track accurately when the target is occluded. To remove the occlusion in the video, combined with the theory of data association, this paper adopts the probabilistic graphical model for multi-target modeling and analysis of the targets relationship in the particle filter framework. Experimental results show that the proposed algorithm can solve the occlusion problem better compared with the traditional algorithm. © 2011 Science Press, Institute of Electronics, CAS and Springer-Verlag Berlin Heidelberg.

关键词： Data association Multi-target tracking Particle filter probabilistic graphical model Video tracking

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Research on Structure Learning Theory and Algorithm Based on probabilistic graphical model

Research on Structure Learning Theory and Algorithm Based on...

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2018 5th International Conference on Electrical & Electronics Engineering and Computer Science(ICEEECS 2018)

作者： Li Feng Department of Mathematics and Statistics Xidian University

probabilistic graphical model can effectively deal with uncertainty reasoning, and learning the probabilistic graphical model from the sample data is an important problem in practical application. The representation of the probabilistic graphical model consists of two parts, parameters and structure. The learning algorithm is also divided into parameter learning and structure learning. In this paper, the structure learning algorithms based on probabilistic graphical model network are introduced in detail. The structural learning algorithms are also summarized based on the differences in the characteristics of structural learning algorithms, and the algorithm of Markov network structure learning is summarized. Finally, this paper points out the open problems of the probabilistic graphical model learning and its further research directions.

关键词： probabilistic graphical model Bayesian network Markov network

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A Joint Community Detection model: Integrating Directed and Undirected probabilistic graphical models via Factor Graph With Attention Mechanism

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IEEE TRANSACTIONS ON BIG DATA 2021年第4期8卷 994-1006页

作者： He, Dongxiao Liu, Huixin Feng, Zhiyong Wang, Xiaobao Jin, Di Song, Wenze Huang, Yuxiao Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China George Washington Univ Data Sci Washington DC 20052 USA

Directed and undirected probabilistic graphical models have been successfully used in community detection in recent years, but existing graphical model based methods usually only use one type of probabilistic graphical model to discover communities. However, directed and undirected graphical models have their own advantages for characterizing different network information (attribute information and network topology). Intuitively, we can make use of the merit of both kinds of models by combining them into a unified model. However, combining directed and undirected graphical models is difficult, as they have different properties which prevent parameter sharing and joint training. In this article, we propose a unified model which integrates directed and undirected graphical models by transforming both them into factor graph. In addition, as network topology and attribute information may contain different degrees of noises, we add a selective attention layer to learn the reliable weight of each type of information source in node granularity. For training the model, we derive an iterative belief propagation algorithm to train all the parameters simultaneously. Extensive experiments on real networks and artificial benchmarks show the superiority of our approach over existing methods.

关键词： graphical models Network topology Semantics Topology probabilistic logic Adaptation models Task analysis Social network analysis community detection probabilistic graphical model factor graph belief propagation

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PETS: Predicting efficiently using temporal symmetries in temporal probabilistic graphical models

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2025年 179卷

作者： Marwitz, Florian Andreas Moeller, Ralf Gehrke, Marcel Univ Hamburg Inst Humanities Ctr Artificial Intelligence Warburgstr 28 D-20354 Hamburg Germany

In Dynamic Bayesian Networks, time is considered discrete: In medical applications, a time step can correspond to, for example, one day. Existing temporal inference algorithms process each time step sequentially, making long-term predictions computationally expensive. We present an exact, GPU-optimizable approach exploiting symmetries over time for prediction queries, which constructs a matrix for the underlying temporal process in a preprocessing step. Additionally, we construct a vector for each query capturing the probability distribution at the current time step. Then, we time-warp into the future by matrix exponentiation. In our empirical evaluation, we show an order of magnitude speedup over the interface algorithm. The work-heavy preprocessing step can be done offline, and the runtime of prediction queries is significantly reduced. Therefore, we can handle application problems that could not be handled efficiently before.

关键词： Dynamic Bayesian network Prediction probabilistic graphical model

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