IEEE transactions on neural networks and learning systems最新文献_第7页

Neighbor-Based Completion for Addressing Incomplete Multiview Clustering

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-13 DOI: 10.1109/tnnls.2025.3540437

Wenbiao Yan, Jihua Zhu, Yiyang Zhou, Jinqian Chen, Haozhe Cheng, Kun Yue, Qinghai Zheng

引用次数: 0

CiRLExplainer: Causality-Inspired Explainer for Graph Neural Networks via Reinforcement Learning

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-13 DOI: 10.1109/tnnls.2025.3543070

Wenya Hu, Jia Wu, Quan Qian

引用次数: 0

Reconstruction-Based Anomaly Localization via Knowledge-Informed Self-Training

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-11 DOI: 10.1109/tnnls.2025.3542229

Cheng Qian, Xiaoxian Lao, Chunguang Li

引用次数: 0

Power Characterization of Noisy Quantum Kernels

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-11 DOI: 10.1109/tnnls.2025.3545545

Yabo Wang, Bo Qi, Xin Wang, Tongliang Liu, Daoyi Dong

引用次数: 0

Cholesky Space for Brain–Computer Interfaces

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-11 DOI: 10.1109/tnnls.2025.3542801

Xingfu Wang, Wenxia Qi, Wenjie Yang, Wei Wang

引用次数: 0

Fusionformer: A Novel Adversarial Transformer Utilizing Fusion Attention for Multivariate Anomaly Detection

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-11 DOI: 10.1109/tnnls.2025.3542719

Chuang Wang, Zidong Wang, Hongli Dong, Stanislao Lauria, Weibo Liu, Yiming Wang, Futra Fadzil, Xiaohui Liu

引用次数: 0

Topology Identification of Weighted Complex Networks Under Intermittent Control and Its Application in Neural Networks.

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-10 DOI: 10.1109/TNNLS.2025.3542505

Huiling Chen, Chunmei Zhang, Han Yang

{"title":"Topology Identification of Weighted Complex Networks Under Intermittent Control and Its Application in Neural Networks.","authors":"Huiling Chen, Chunmei Zhang, Han Yang","doi":"10.1109/TNNLS.2025.3542505","DOIUrl":"https://doi.org/10.1109/TNNLS.2025.3542505","url":null,"abstract":"Topology identification of stochastic complex networks is an important topic in network science. In modern identification techniques under a continuous framework, the controller has a negative dynamic gain (feedback gain), such that stochastic LaSalle's invariance principle (SLIP) is directly satisfied. In this article, the topology identification of stochastic complex networks is studied under aperiodic intermittent control (AIC). It is noteworthy that the AIC has a rest time, which indicates the SLIP is not valid since there is no negative feedback gained during this period. This motivates us to find other methods to obtain identification criteria. In this study, the graph-theoretic method and the stochastic analysis technique are integrated to obtain the almost surely exponential synchronization of drive-response networks. Furthermore, this integration enables the topology identification criteria of the drive network to be derived, which differs from previous work that directly utilized SLIP. It is worth mentioning that the topology identification criteria under the stochastic framework are first proposed based on the AIC in this work. The control strategy not only reduces the control cost but also makes it easier to operate. To enhance the application value of the network model, regime-switching diffusions, multiple weights, and nonlinear couplings are simultaneously considered. Finally, the proposed identification criteria are tested by using neural networks. At the same time, the validity of the theoretical results is further proved by numerical simulations.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction.

IF 10.2 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-10 DOI: 10.1109/TNNLS.2025.3543947

Basit Alawode, Sajid Javed

{"title":"Learning Spatial-Temporal Regularized Tensor Sparse RPCA for Background Subtraction.","authors":"Basit Alawode, Sajid Javed","doi":"10.1109/TNNLS.2025.3543947","DOIUrl":"10.1109/TNNLS.2025.3543947","url":null,"abstract":"Background subtraction in videos is a core challenge in computer vision, aiming to accurately identify moving objects. Robust principal component analysis (RPCA) has emerged as a promising unsupervised (US) paradigm for this task, showing strong performance on various benchmark datasets. Building on RPCA, tensor RPCA (TRPCA) variants have further enhanced background subtraction performance. However, current TRPCA methods often treat moving object pixels independently, lacking spatial-temporal structured-sparsity constraints. This limitation leads to performance degradation in scenarios with dynamic backgrounds, camouflage, and camera jitter. In this work, we introduce a novel spatial-temporal regularized tensor sparse RPCA algorithm to address these issues. By incorporating normalized graph-Laplacian matrices into the sparse component, we enforce spatial-temporal regularization. We construct two graphs-one across spatial locations and another across temporal slices-to guide regularization. By maximizing our objective function, we ensure that the tensor sparse component aligns with the spatiotemporal eigenvectors of the graph-Laplacian matrices, preserving disconnected moving object pixels. We formulate a new objective function and employ batch and online-based optimization methods to jointly optimize background-foreground separation and spatial-temporal regularization. Experimental evaluation on six publicly available datasets demonstrates the superior performance of our algorithm compared to existing methods.","PeriodicalId":13303,"journal":{"name":"IEEE transactions on neural networks and learning systems","volume":"PP ","pages":""},"PeriodicalIF":10.2,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143596879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Broad Learning System Based on Fractional Feature Optimization 基于分数特征优化的广泛学习系统

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-07 DOI: 10.1109/tnnls.2025.3540076

Dan Zhang, Tong Zhang, C. L. Philip Chen, Tao Zhang

引用次数: 0

Mode Hypergraph Neural Network

IF 10.4 1区计算机科学

IEEE transactions on neural networks and learning systems Pub Date : 2025-03-07 DOI: 10.1109/tnnls.2025.3542176

Shuyi Ji, Yifan Feng, Donglin Di, Shihui Ying, Yue Gao

引用次数: 0