IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第10页

BAUODNET for Class Imbalance Learning in Underwater Object Detection 基于BAUODNET的水下目标检测中的类不平衡学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-25 DOI: 10.1109/TETCI.2024.3462249

Long Chen;Haohan Yu;Xirui Dong;Yaxin Li;Jialie Shen;Jiangrong Shen;Qi Xu

{"title":"BAUODNET for Class Imbalance Learning in Underwater Object Detection","authors":"Long Chen;Haohan Yu;Xirui Dong;Yaxin Li;Jialie Shen;Jiangrong Shen;Qi Xu","doi":"10.1109/TETCI.2024.3462249","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462249","url":null,"abstract":"Underwater object detection is of great significance for various applications in underwater scenes. However, class imbalance issue is still an unsolved bottleneck for current underwater object detection algorithms. It leads to large discrepancies in the detection precision among different classes that the dominant classes with more training data achieve higher precision while the minority classes with less training data achieve much lower precision. In this paper, we propose a balanced underwater object detection network (BAUODNET) to address the class imbalance issue by exploiting two techniques, i.e., the style augmentation technique and the example re-weighting technique. Firstly, we propose a class-wise style augmentation (CWSA) algorithm to augment the training data for the minority classes that generates different colors, textures and contrasts for the minority classes whilst preserving geometry. The augmented dataset possesses more balanced data distribution; Secondly, we exploit the the focal loss to re-weight the examples during the training of the deep detector, it down-weights the loss assigned to the well-detected examples from the dominant classes and focuses on learning undetected hard examples from the minority classes. Extensive experiments show the effectiveness of CWSA and focal loss for addressing the class imbalance problem in underwater scenes, BAUODNET obtains 49.5% mAP on URPC2017 and 66.8% mAP on URPC2018, achieving state-of-the-art or comparable performance on URPC2017 and URPC2018.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2452-2461"},"PeriodicalIF":5.3,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

CIGF-Net: Cross-Modality Interaction and Global-Feature Fusion for RGB-T Semantic Segmentation 跨模态交互和全局特征融合的RGB-T语义分割

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-23 DOI: 10.1109/TETCI.2024.3462168

Zhiwei Zhang;Yisha Liu;Weimin Xue;Yan Zhuang

{"title":"CIGF-Net: Cross-Modality Interaction and Global-Feature Fusion for RGB-T Semantic Segmentation","authors":"Zhiwei Zhang;Yisha Liu;Weimin Xue;Yan Zhuang","doi":"10.1109/TETCI.2024.3462168","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3462168","url":null,"abstract":"RGB-T semantic segmentation aims to enhance the robustness of segmentation methods in complex environments by utilizing thermal information. To facilitate the effective interaction and fusion of multimodal information, we propose a novel Cross-modality Interaction and Global-feature Fusion Network, namely CIGF-Net. In each feature extraction stage, we propose a Cross-modality Interaction Module (CIM) to enable effective interaction between the RGB and thermal modalities. CIM utilizes channel and spatial attention mechanisms to process the feature information from both modalities. By encouraging cross-modal information exchange, the CIM facilitates the integration of complementary information and improves the overall segmentation performance. Subsequently, the Global-feature Fusion Module (GFM) is proposed to focus on fusing the information provided by the CIM. GFM assigns different weights to the multimodal features to achieve cross-modality fusion. Experimental results show that CIGF-Net achieves state-of-the-art performance on RGB-T image semantic segmentation datasets, with a remarkable 60.8 mIoU on the MFNet dataset and 86.93 mIoU on the PST900 dataset.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2440-2451"},"PeriodicalIF":5.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Toward Automatic Market Making: An Imitative Reinforcement Learning Approach With Predictive Representation Learning 迈向自动做市：一种带有预测表示学习的模仿强化学习方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-23 DOI: 10.1109/TETCI.2024.3451476

Siyuan Li;Yafei Chen;Hui Niu;Jiahao Zheng;Zhouchi Lin;Jian Li;Jian Guo;Zhen Wang

{"title":"Toward Automatic Market Making: An Imitative Reinforcement Learning Approach With Predictive Representation Learning","authors":"Siyuan Li;Yafei Chen;Hui Niu;Jiahao Zheng;Zhouchi Lin;Jian Li;Jian Guo;Zhen Wang","doi":"10.1109/TETCI.2024.3451476","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451476","url":null,"abstract":"Market making (MM) is a crucial trading problem, where a market maker stands ready to buy and sell the asset at a publicly quoted price to provide market liquidity continuously. The primary challenges in market making include position risk, liquidity risk, and adverse selection. Emerging research works investigate applying reinforcement learning (RL) techniques to derive automatic MM strategies. However, existing methods mainly focus on addressing inventory risk using only single-level quotes, which restricts the trading flexibility. In this paper, we shed light on the optimization of market makers' returns under a smaller risk while ensuring market liquidity and depth. This paper proposes a novel RL-based market-making strategy <italic>Predictive and Imitative Market Making Agent (PIMMA). First, to ensure adequate liquidity, we design an action space to enable stably allocating orders of multi-level volumes and prices. Beyond that, we apply queue position information from these multi-price levels to encode them in the state representations. Second, aiming at alleviating adverse selection, we draw auxiliary signals into state representation and design a representation learning network structure to catch implicit information from the price-volume fluctuations. Finally, we develop a novel reward function to earn a fortune while avoiding holding a large inventory. With a provided expert demonstration, our method augments the RL objective with imitation learning and learns an effective MM policy. Experiments are conducted to evaluate the proposed method based on realistic historical data, and the results demonstrate PIMMA outperforms RL-based strategy in the perspectives of earning decent revenue and information by adopting the multi-risk aversion strategy.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2427-2439"},"PeriodicalIF":5.3,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

SODSR: A Three-Stage Small Object Detection via Super-Resolution Using Optimizing Combination SODSR：一种基于优化组合的三阶段超分辨率小目标检测方法

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-20 DOI: 10.1109/TETCI.2024.3452749

Xiaoyong Mei;Kejin Zhang;Changqin Huang;Xiao Chen;Ming Li;Zhao Li;Weiping Ding;Xindong Wu

{"title":"SODSR: A Three-Stage Small Object Detection via Super-Resolution Using Optimizing Combination","authors":"Xiaoyong Mei;Kejin Zhang;Changqin Huang;Xiao Chen;Ming Li;Zhao Li;Weiping Ding;Xindong Wu","doi":"10.1109/TETCI.2024.3452749","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3452749","url":null,"abstract":"Face detection is a fundamental task in computer vision, yet remains challenging in educational settings due to the presence of objects of various sizes. Subpar detection can significantly impede subsequent tasks' performance. To address this, we present a novel framework, Small Object Detection Super Resolution (SODSR), inspired by super resolution (SR) techniques for feature-level images. SODSR comprises three stages: (1) Constructing a 3D model evaluation matrix to select optimal model combinations based on detection accuracy and image quality metrics. (2) Employing Double-thread FDN in the first stage to preprocess images, enhancing feature resolution for potential facial objects. (3) Leveraging Multi-head HyperNet in the second stage to augment face feature detection and improve accuracy. Finally, in the third stage, we introduce AFPGAN, a facial prior feature enhancement network, coupled with StyleGAN2 for texture and contour detail enhancement. Experimental results demonstrate that SODSR outperforms existing small object detection (SOD) models in both accuracy and visual fidelity.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2410-2426"},"PeriodicalIF":5.3,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition 基于一致张量分解的张量化多核聚类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-19 DOI: 10.1109/TETCI.2024.3425329

Fei Qi;Junyu Li;Yue Zhang;Weitian Huang;Bin Hu;Hongmin Cai

{"title":"Tensorlized Multi-Kernel Clustering via Consensus Tensor Decomposition","authors":"Fei Qi;Junyu Li;Yue Zhang;Weitian Huang;Bin Hu;Hongmin Cai","doi":"10.1109/TETCI.2024.3425329","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3425329","url":null,"abstract":"Multi-kernel clustering aims to learn a fused kernel from a set of base kernels. However, conventional multi-kernel clustering methods typically suffer from inherent limitations in exploiting the interrelations and complementarity between the kernels. The noises and redundant information from original base kernels also lead to contamination of the fused kernel. To address these issues, this paper presents a Tensorlized Multi-Kernel Clustering (TensorMKC) method. The proposed TensorMKC stacks kernel matrices into a kernel tensor along the kernel space. To attain consensus extraction while mitigating the impact of noise, we incorporate the tensor low-rank constraint into the process of learning base kernels. Subsequently, a tensor-based weighted fusion strategy is employed to integrate the refined base kernels, yielding an optimized fused kernel for clustering. The process of kernel learning is formulated as a joint minimization problem to seek the promising fusion solution. Through extensive comparative experiments with fifteen popular methods on ten benchmark datasets from various fields, the results demonstrate that TensorMKC exhibits superior performance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"406-418"},"PeriodicalIF":5.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Neuromorphic Auditory Perception by Neural Spiketrum 神经尖谱的神经形态听觉感知

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-18 DOI: 10.1109/TETCI.2024.3419711

Huajin Tang;Pengjie Gu;Jayawan Wijekoon;MHD Anas Alsakkal;Ziming Wang;Jiangrong Shen;Rui Yan;Gang Pan

{"title":"Neuromorphic Auditory Perception by Neural Spiketrum","authors":"Huajin Tang;Pengjie Gu;Jayawan Wijekoon;MHD Anas Alsakkal;Ziming Wang;Jiangrong Shen;Rui Yan;Gang Pan","doi":"10.1109/TETCI.2024.3419711","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3419711","url":null,"abstract":"Neuromorphic computing holds the promise to achieve the energy efficiency and robust learning performance of biological neural systems. To realize the promised brain-like intelligence, it needs to solve the challenges of the neuromorphic hardware architecture design of biological neural substrate and the hardware amicable algorithms with spike-based encoding and learning. Here we introduce a neural spike coding model termed spiketrum, to characterize and transform the time-varying analog signals, typically auditory signals, into computationally efficient spatiotemporal spike patterns. It minimizes the information loss occurring at the analog-to-spike transformation and possesses informational robustness to neural fluctuations and spike losses. The model provides a sparse and efficient coding scheme with precisely controllable spike rate that facilitates training of spiking neural networks in various auditory perception tasks. We further investigate the algorithm-hardware co-designs through a neuromorphic cochlear prototype which demonstrates that our approach can provide a systematic solution for spike-based artificial intelligence by fully exploiting its advantages with spike-based computation.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"292-303"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability 联邦法：具有个体公平和联盟稳定性的价值意识联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-18 DOI: 10.1109/TETCI.2024.3446458

Jianfeng Lu;Hangjian Zhang;Pan Zhou;Xiong Wang;Chen Wang;Dapeng Oliver Wu

{"title":"FedLaw: Value-Aware Federated Learning With Individual Fairness and Coalition Stability","authors":"Jianfeng Lu;Hangjian Zhang;Pan Zhou;Xiong Wang;Chen Wang;Dapeng Oliver Wu","doi":"10.1109/TETCI.2024.3446458","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3446458","url":null,"abstract":"A long-standing problem remains with the heterogeneous clients in Federated Learning (FL), who often have diverse gains and requirements for the trained model, while their contributions are hard to evaluate due to the privacy-preserving training. Existing works mainly rely on single-dimension metric to calculate clients' contributions as aggregation weights, which however may damage the social fairness, thus discouraging the cooperation willingness of worse-off clients and causing the revenue instability. To tackle this issue, we propose a novel incentive mechanism named <italic>FedLaw to effectively evaluate clients' contributions and further assign aggregation weights. Specifically, we reuse the local model updates and model the contribution evaluation process as a convex coalition game among multiple players with a non-empty core. By deriving a closed-form expression of the Shapley value, we solve the game core in quadratic time. Moreover, we theoretically prove that <italic>FedLaw guarantees <italic>individual fairness, <italic>coalition stability, <italic>computational efficiency, <italic>collective rationality, <italic>redundancy, <italic>symmetry, <italic>additivity, <italic>strict desirability, and <italic>individual monotonicity, and also show that <italic>FedLaw can achieve a constant convergence bound. Extensive experiments on four real-world datasets validate the superiority of <italic>FedLaw in terms of model aggregation, fairness, and time overhead compared to the state-of-the-art five baselines. Experimental results show that <italic>FedLaw is able to reduce the computation time of contribution evaluation by about 12 times and improve the global model performance by about 2% while ensuring fairness.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 1","pages":"1049-1062"},"PeriodicalIF":5.3,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Utilizing a DenseSwin Transformer Model for the Classification of Maize Plant Pathology in Early and Late Growth Stages: A Case Study of Its Utilization Among Zambian Farmers 利用DenseSwin变压器模型对玉米生长早期和晚期植物病理学进行分类：赞比亚农民对其利用的案例研究

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-17 DOI: 10.1109/TETCI.2024.3444603

Chiuzu Chilumbu;Qi-Xian Huang;Hung-Min Sun

{"title":"Utilizing a DenseSwin Transformer Model for the Classification of Maize Plant Pathology in Early and Late Growth Stages: A Case Study of Its Utilization Among Zambian Farmers","authors":"Chiuzu Chilumbu;Qi-Xian Huang;Hung-Min Sun","doi":"10.1109/TETCI.2024.3444603","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3444603","url":null,"abstract":"Maize, which is the primarycrop in many sub-Saharan countries, including Zambia, is susceptible to a wide range of diseases that have a significant impact on food production. To tackle this challenge and improve disease detection efficiency, deep learning methods have been employed to accurately classify and identify plant diseases. In recent times, manual inspection of maize fields for disease detection has been the standard practice in many parts of Zambia. However, this approach is not only time-consuming but also impractical for large-scale agricultural operations. Hence, the development of precise and automated classification models has become crucial in modern agriculture. In this study, we propose a novel deep-learning model called DenseSwin, specifically designed for maize disease classification in both the early visible stage and late indisputable stage of the disease. DenseSwin combines the strengths of densely connected convolution blocks with a shifted windows-based multi-head self-attention mechanism. This unique fusion of techniques enables the model to effectively capture intricate patterns and features in maize plant images, thereby enhancing disease classification performance. Through extensive experimentation and evaluation, DenseSwin achieves an impressive accuracy of 97.18%. These results highlight the model's remarkable ability to accurately detect and classify maize diseases, offering promising potential for real-world applications in agricultural settings, particularly in Zambia.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1860-1872"},"PeriodicalIF":5.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-Ship Dynamic Weapon-Target Assignment via Cooperative Distributional Reinforcement Learning With Dynamic Reward 基于动态奖励协同分布强化学习的多舰动态武器目标分配

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-17 DOI: 10.1109/TETCI.2024.3451338

Zhe Peng;Zhifeng Lu;Xiao Mao;Feng Ye;Kuihua Huang;Guohua Wu;Ling Wang

{"title":"Multi-Ship Dynamic Weapon-Target Assignment via Cooperative Distributional Reinforcement Learning With Dynamic Reward","authors":"Zhe Peng;Zhifeng Lu;Xiao Mao;Feng Ye;Kuihua Huang;Guohua Wu;Ling Wang","doi":"10.1109/TETCI.2024.3451338","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3451338","url":null,"abstract":"In fleet air defense, the efficient coordination of multiple ships to complete weapon-target assignment has always been a critical challenge, primarily due to the varying combat capabilities and duties associated with each ship. Consequently, the traditional “weapon-target” assignment mode has turned into a “ship-weapon-target” assignment mode in the multi-ship dynamic weapon-target assignment (MS-DWTA) problem we proposed, with a larger solution space. In this problem, different ships possess distinct attributes, such as defense duties, weapon types, and loaded missile quantities. To solve this problem, we proposed an Attention enhanced multi-agent Distributional reinforcement learning method with Dynamic Reward (ADDR). Different from standard reinforcement learning method, ADDR learns to estimate the distribution, as opposed to only the expectation of future return, enabling better adaptation to air defense scenarios with significant randomness. The multi-head attention network integrates both the ship situation and the target situation to appropriately adjust the output of each agent, which explicitly considers the agent-level impact of ships to the whole fleet. Moreover, due to the missile fight time, ships may not immediately receive rewards after executing actions. To address this delayed phenomenon, we designed a dynamic reward mechanism to accurately adjust the delayed rewards. Through extensive simulation experiments, ADDR has demonstrated superior performance over multiple evaluation metrics.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1843-1859"},"PeriodicalIF":5.3,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Multi-View Clustering With Consistent Local Structure-Guided Graph Fusion 基于一致性局部结构引导图融合的多视图聚类

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2024-09-16 DOI: 10.1109/TETCI.2024.3423459

Naiyao Liang;Zuyuan Yang;Wei Han;Zhenni Li;Shengli Xie

引用次数: 0