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Noncooperative Model Predictive Game With Markov Jump Graph

IEEE/CAA Journal of Automatica Sinica 2023年第4期10卷 931-944页

作者： Yang Xu Yuan Yuan Zhen Wang Xuelong Li School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China School of Computer Science and Center for Optical Imagery Analysis and Learning Northwestern Polytechnical Universityand also with the School of CybersecurityNorthwestern Polytechnical UniversityXi’an 710072China School of Artificial Intelligence Optics and Electronics(iOPEN)Northwestern Polytechnical Universityand also with the Key Laboratory of Intelligent Interaction and ApplicationsNorthwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi’an 710072China

In this paper,the distributed stochastic model predictive control(MPC)is proposed for the noncooperative game problem of the discrete-time multi-player systems(MPSs)with the undirected Markov jump *** reflect the reality,the state and input constraints have been considered along with the external *** iterative algorithm is designed such that model predictive noncooperative game could converge to the socalledε-Nash equilibrium in a distributed *** conditions are established to guarantee the convergence of the proposed *** addition,a set of easy-to-check conditions are provided to ensure the mean-square uniform bounded stability of the underlying ***,a numerical example on a group of spacecrafts is studied to verify the effectiveness of the proposed method.

关键词： Markov jump graph model predictive control(MPC) multi-player systems(MPSs) noncooperative game ε-Nash equilibrium

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Higher-order temporal interactions promote the cooperation in the multiplayer snowdrift game

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science China(Information sciences) 2023年第12期66卷 138-153页

作者： Yan XU Juan WANG Chengyi XIA Zhen WANG School of Computer Science and Engineering Tianjin University of Technology School of Electrical Engineering and Automation Tianjin University of Technology School of Artificial Intelligence Tiangong University School of Cyberspace and Center for OPTical IMagery Analysis and Learning(OPTIMAL) Northwestern Polytechnical University

To explore evolutionary dynamics of collective behaviors within the interconnected population,previous studies usually map non-pairwise interactions to higher-order static networks. However, from human communications to chemical reactions and biological systems, interactions often change over time, which cannot be simply described by higher-order static networks. In this study, we introduce time effects into higher-order networks and correspondingly investigate the evolutionary dynamics of multiplayer snowdrift games on higher-order temporal networks. Specifically, extensive simulations from four empirical datasets reveal that(1) the temporal effect of higher-order networks can facilitate the evolution of cooperation;(2) the higher-order topology can enhance the emergence of cooperation within a certain range of parameters;(3) the contribution of temporal burstiness and participants burstiness to cooperation is reversed. Furthermore,we theoretically demonstrate that the higher-order structure will suppress the propagation of defection in temporal networks. Our findings offer a new avenue for studying the evolution of altruistic behaviors in realistic complex networks.

关键词： collective behaviors non-pairwise interactions multiplayer snowdrift games temporal networks higher-order complex networks

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Robust Semi-Supervised Deep Nonnegative Matrix Factorization with Constraint Propagation for Data Representation

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IEEE Transactions on Big Data 2025年

作者： Peng, Siyuan Yin, Jingxing Yang, Zhijing Nie, Feiping Chen, Badong Guangdong University of Technology School of Information Engineering 510006 China Northwestern Polytechnical University School of Computer Science Center for OPTical IMagery Analysis and Learning Xi'an710072 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China

Deep nonnegative matrix factorization (DNMF) technique has attached much great attention in recent year, since it can effectively discover the underlying hierarchical structure of complex data. However, most existing unsupervised and semi-supervised DNMF approaches not only suffer from the noisy data seriously, but also fail to enhance the decomposition quality of DNMF obviously by using the obtained supervisory information. To overcome these drawbacks, a robust semi-supervised DNMF method, called the correntropy based semi-supervised DNMF with constraint propagation (CSDCP), is proposed in this paper for learning a compact and meaningful data representation from the original data. Particularly, instead of adopting the traditional Frobenius norm, CSDCP employs the nonlinear and local similarity measure (e.g., correntropy) as the loss function in DNMF to enhance the robustness of DNMF for the noisy data. In addition, the hypergraph based constraint propagation (HCP) algorithm is adopted in CSDCP to exploit the limited supervisory information fully for capturing good data representation. Moreover, algorithm analysis of CSDCP is presented in this paper, including convergence analysis, robustness analysis supervised information analysis, and computational complexity. Extensive experimental results have illustrated that, in comparison to the most related DNMF approaches, CSDCP usually has better clustering results on six nonnegative datasets in clustering tasks. © 2015 IEEE.

关键词： Self-supervised learning

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Multitype View of Knowledge Contrastive learning for Recommendation

SSRN

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SSRN 2024年

作者： Zhang, Zhi-Hao Yang, Xiao-Jun Wu, Yang-Hui Wang, Jing Nie, Fei-Ping Guangdong University of Technology Guangzhou510006 China The School of Integrated Circuits Guangdong University of Technology Guangzhou510006 China The School of Computer Science The Center for Optical Imagery Analysis and Learning Northwestern Polytechnical University Xi’an710072 China

Graph Neural Networks (GNNs) are playing an increasingly vital role in the field of recommender systems. To improve knowledge perception within GNNs, contrastive learning has been applied and has proven to be highly effective. GNNs have the ability to aggregate diverse knowledge and integrate topologies, while contrastive learning seeks supervisory signals from the model data. The combination of GNNs and contrastive learning can improve recommendations. However, thoughtless or incomplete contrastive learning settings limit the effectiveness of GNNs-based recommender systems in learning knowledge from knowledge and interaction graphs. To better exploit the valuable information within knowledge graphs, we propose a novel multitype view of knowledge contrastive learning for recommendations (MVKC) *** MVKC model generates hierarchical views and augmented views in two modules, performing cross-hierarchicalview and cross-augmented-view contrastive learning and mining graph features in a self-supervised manner. The hierarchical views consist of global and local parts at multiple levels, while the augmented views are fused from the augmented knowledge graph and augmented interaction graph in our augmented processing. These features allow the MVKC model to alleviate the sparsity of user-item interaction graphs, suppress knowledge graph noise, and filter long-tail entities, which has been proven extremely important for a recommendation. The MVKC model also has strong anti-interference ability and robustness, which is crucial for a well-established model. Our experiments with three public datasets demonstrate that the MVKC model outperforms current state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Binary Classification with Supervised-like Biclustering and Adaboost 7

Binary Classification with Supervised-like Biclustering and ...

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7th International Conference on Information science and Control Engineering, ICISCE 2020

作者： Sun, Jianjun Huang, Qinghua Northwestern Polytechnical University School of Computer Science Center for OPTical IMagery Analysis and Learning Xi'an710072 China

ISBN: (纸本)9781728164069

Biclustering based classification methods have achieved great success. In existing biclustering based classification methods the residue score threshold used for searching biclusters is fixed and the number of mined useful biclusters is small. To solve the two problems, in this study we proposed a novel binary classification method based on supervised-like biclustering and adaboost. Supervised-like means the biclusters is searched twice with two different bicluster quality indicators MSD and weights instead of unique indicator MSR in other biclustering based classification methods. In initial search, the residue score threshold is identical for all biclusters. In the second search, some biclusters of low quality are searched again with smaller residue threshold. Besides, due to some limitations, many biclusters cannot be found. To obtain more biclusters, two additional operations are adopted. In the proposed method, we initially mine column constant biclusters from datasets, then transform the biclusters to weak classifiers. Through adaboost, the initial strong binary classifier can be constructed. Finally, with supervised-like strategy, the better final strong binary classifier can be obtained. To verify the performance of the proposed method, it is compared with seven binary classification methods on two datasets. Experimental results demonstrated that the proposed method outperformed other binary classification methods. © 2020 IEEE.

关键词： Adaptive boosting

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Mobile person re-identification with a lightweight trident CNN

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science China(Information sciences) 2020年第11期63卷 293-297页

作者： Mingfu XIONG Dan CHEN Xiaoqiang LU Engineering Research Center of Hubei Province for Clothing Information School of Mathematics and Computer ScienceWuhan Textile University National Engineering Research Center for Multimedia Software School of Computer ScienceWuhan University Center for OPTical IMagery Analysis and Learning (OPTIMAL) State Key Laboratory of Transient Optics and Photonics Xi'an Institute of Optics and Precision MechanicsChinese Academy of Sciences

Dear editor,Person re-identification (re-ID) aims to recognize or associate a person and time at non-overlapping physical locations after the person had been previously observed visually elsewhere. This technology is ... 详细信息

关键词： Smart Mobile Device Person Re-identification Trident CNN Smart City Video Surveillance

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MFI: Multi-range Feature Interchange for Video Action Recognition

MFI: Multi-range Feature Interchange for Video Action Recogn...

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International Conference on Pattern Recognition

作者： Sikai Bai Qi Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning Northwestern Polytechnical University Xi'an Shaanxi P. R. China

Short-range motion features and long-range dependencies are two complementary and vital cues for action recognition in videos, but it remains unclear how to efficiently and effectively extract these two features. In this paper, we propose a novel network to capture these two features in a unified 2D framework. Specifically, we first construct a Short-range Temporal Interchange (STI) block, which contains a Channels-wise Temporal Interchange (CTI) module for encoding short-range motion features. Then a Graph-based Regional Interchange (GRI) module is built to present long-range dependencies using graph convolution. Finally, we replace original bottleneck blocks in the ResNet with STI blocks and insert several GRI modules between STI blocks, to form a Multi-range Feature Interchange (MFI) Network. Practically, extensive experiments are conducted on three action recognition datasets (i.e., Something-Something V1, HMDB51, and UCF101), which demonstrate that the proposed MFI network achieves impressive results with very limited computing cost.

关键词： Three-dimensional displays Convolution Feature extraction Encoding Pattern recognition optical flow Videos

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Dependence-Guided Multi-View Clustering

Dependence-Guided Multi-View Clustering

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Xia Dong Danyang Wu Feiping Nie Rong Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an P. R. China School of Cybersecurity and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an P. R. China

In this paper, we propose a novel approach called dependence-guided multi-view clustering (DGMC). Our model enhances the dependence between unified embedding learning and clustering, as well as promotes the dependence between unified embedding and embedding of each view. Specifically, DGMC learns a unified embedding and partitions data in a joint fashion, thus the clustering results can be directly obtained. A kernel dependence measure is employed to learn a unified embedding by forcing it to be close to different views, thus the complex dependence among different views can be captured. Moreover, an implicit-weight learning mechanism is provided to ensure the diversity of different views. An efficient algorithm with rigorous convergence analysis is derived to solve the proposed model. Experimental results demonstrate the advantages of the proposed method over the state of the arts on real-world datasets.

关键词： learning systems Analytical models Conferences Clustering methods Signal processing algorithms Signal processing Iterative methods

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A Rank-Constrained Clustering Algorithm with Adaptive Embedding

A Rank-Constrained Clustering Algorithm with Adaptive Embedd...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Shenfei Pei Feiping Nie Rong Wang Xuelong Li School of Computer Science and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an P. R. China School of Cybersecurity and Center for OPTical IMagery Analysis and Learning (OPTIMAL) Northwestern Polytechnical University Xi’an P. R. China

Most spectral clustering algorithms obtain firstly the embedding based on the similarity matrix of the input data and then discretize the embedding to get the final clustering result. So, their performance is greatly affected by the noise of the similarity matrix, and the two-step strategy may lead to a suboptimal result. In this article, a novel Rank-Constrained clustering algorithm with Adaptive Embedding called RCAE is proposed, where the spectral embedding and the clustering structure are learned simultaneously, so, the influence of noise on performance is greatly reduced. In addition, a rank constraint is adopted in our model, thus, the connectivity matrix with exactly c (the number of clusters to construct) connected components can be learned, therefore, the final clustering result can be obtained according to the connected components. Experiments on several benchmark datasets validate the superiority of the proposed methods, compared to the several state-of-the-art clustering algorithms [GitHub].

关键词： Adaptation models Conferences Clustering methods Clustering algorithms Signal processing algorithms Benchmark testing Signal processing

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Open Set Domain Recognition via Attention-Based GCN and Semantic Matching Optimization

Open Set Domain Recognition via Attention-Based GCN and Sema...

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International Conference on Pattern Recognition

作者： Xinxing He Yuan Yuan Zhiyu Jiang School of Computer Science and Center for OPTical IMagery Analysis and Learning(OPTIMAL) Northwestern Polytechnical University Xi'an Shaanxi P.R. China

Open set domain recognition has got the attention in recent years. The task aims to specifically classify each sample in the practical unlabeled target domain, which consists of all known classes in the manually labeled source domain and target-specific unknown categories. The absence of annotated training data or auxiliary attribute information for unknown categories makes this task especially difficult. Moreover, exiting domain discrepancy in label space and data distribution further distracts the knowledge transferred from known classes to unknown classes. To address these issues, this work presents an end-to-end model based on attention-based GCN and semantic matching optimization, which first employs the attention mechanism to enable the central node to learn more discriminating representations from its neighbors in the knowledge graph. Moreover, a coarse-to-fine semantic matching optimization approach is proposed to progressively bridge the domain gap. Experimental results validate that the proposed model not only has superiority on recognizing the images of known and unknown classes, but also can adapt to various openness of the target domain.

关键词： Training Limiting Image recognition Target recognition Semantics Training data Network architecture

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