IEEE Transactions on Emerging Topics in Computational Intelligence最新文献_第5页

Generative Network Correction to Promote Incremental Learning 生成网络纠错促进增量学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-05 DOI: 10.1109/TETCI.2025.3543370

Justin Leo;Jugal Kalita

{"title":"Generative Network Correction to Promote Incremental Learning","authors":"Justin Leo;Jugal Kalita","doi":"10.1109/TETCI.2025.3543370","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543370","url":null,"abstract":"Neural networks are often designed for closed environments that are not open to acquisition of new knowledge. Incremental learning techniques allow neural networks to adapt to changing environments, but these methods often encounter challenges causing models to suffer from low classification accuracies. The main problem faced is catastrophic forgetting and this problem is more harmful when using incremental strategies compared to regular tasks. Some known causes of catastrophic forgetting are weight drift and inter-class confusion; these problems cause the network to erroneously fuse trained classes or to forget a learned class. This paper addresses these issues by focusing on data pre-processing and using network feedback corrections for incremental learning. Data pre-processing is important as the quality of the training data used affects the network's ability to maintain continuous class discrimination. This approach uses a generative model to modify the data input for the incremental model. Network feedback corrections would allow the network to adapt to newly found classes and scale based on network need. With combination of generative data pre-processing and network feedback, this paper proposes an approach for efficient long-term incremental learning. The results obtained are compared with similar state-of-the-art algorithms and show high incremental accuracy levels.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2334-2343"},"PeriodicalIF":5.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148129","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

A Cross-Domain Recommendation Model Based on Asymmetric Vertical Federated Learning and Heterogeneous Representation 基于非对称垂直联邦学习和异构表示的跨领域推荐模型

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-04 DOI: 10.1109/TETCI.2025.3543313

Wanjing Zhao;Yunpeng Xiao;Tun Li;Rong Wang;Qian Li;Guoyin Wang

{"title":"A Cross-Domain Recommendation Model Based on Asymmetric Vertical Federated Learning and Heterogeneous Representation","authors":"Wanjing Zhao;Yunpeng Xiao;Tun Li;Rong Wang;Qian Li;Guoyin Wang","doi":"10.1109/TETCI.2025.3543313","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3543313","url":null,"abstract":"Cross-domain recommendation meets the personalized needs of users by integrating user preference features from different fields. However, the current cross-domain recommendation algorithm needs to be further strengthened in terms of privacy protection. This paper proposes a cross-domain recommendation model based on asymmetric vertical federated learning and heterogeneous representation. This model can improve the accuracy and diversity of recommendations under the premise of privacy protection. Firstly, we propose a privacy set intersection model based on data augmentation. This model improves the data imbalance among participants by introducing obfuscation sets. It can conceal the true data volumes of each party, thereby protecting the sensitive information of weaker parties. Secondly, we propose a heterogeneous representation method based on a walking strategy incorporating interaction timing. This method combines users' recent interests to generate node sequences that reflect the characteristics of user preferences. Then we use the Skip-Gram model to represent the node sequence in a low-dimensional embedding. Finally, we propose a cross-domain recommendation model based on vertical federated learning. This model uses the federated factorization machine to complete the interest prediction and protect the privacy data security of each domain. Experiments show that on the real data set, the model can further guarantee the data security of each participant in the asymmetric federated learning. It can also improve the recommendation accuracy on the target domain.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2344-2358"},"PeriodicalIF":5.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148137","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

Modeling of Spiking Neural Network With Optimal Hidden Layer via Spatiotemporal Orthogonal Encoding for Patterns Recognition 基于时空正交编码的最优隐层脉冲神经网络模式识别建模

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-03 DOI: 10.1109/TETCI.2025.3537944

Zenan Huang;Yinghui Chang;Weikang Wu;Chenhui Zhao;Hongyan Luo;Shan He;Donghui Guo

{"title":"Modeling of Spiking Neural Network With Optimal Hidden Layer via Spatiotemporal Orthogonal Encoding for Patterns Recognition","authors":"Zenan Huang;Yinghui Chang;Weikang Wu;Chenhui Zhao;Hongyan Luo;Shan He;Donghui Guo","doi":"10.1109/TETCI.2025.3537944","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3537944","url":null,"abstract":"The Spiking Neural Network (SNN) diverges from conventional rate-based network models by showcasing remarkable biological fidelity and advanced spatiotemporal computation capabilities, precisely converting input spike sequences into firing activities. This paper introduces the Spiking Optimal Neural Network (SONN), a model that integrates spiking neurons with spatiotemporal orthogonal polynomials to enhance pattern recognition capabilities. SONN innovatively integrates orthogonal polynomials and complex domain transformations seamlessly into neural dynamics, aiming to elucidate neural encoding and enhance cognitive computing capabilities. The dynamic integration of SONN enables continuous optimization of encoding methodologies and layer structures, showcasing its adaptability and refinement over time. Fundamentally, the model provides an adjustable method based on orthogonal polynomials and the corresponding complex-valued neuron model, striking a balance between network scalability and output accuracy. To evaluate its performance, SONN underwent experiments using datasets from the UCI Machine Learning Repository, the Fashion-MNIST dataset, the CIFAR-10 dataset and neuromorphic DVS128 Gesture dataset. The results show that smaller-sized SONN architectures achieve comparable accuracy in benchmark datasets compared to other SNNs.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2194-2207"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148164","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

MSDT: Multiscale Diffusion Transformer for Multimodality Image Fusion 用于多模态图像融合的多尺度扩散变压器

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-03 DOI: 10.1109/TETCI.2025.3542146

Caifeng Xia;Hongwei Gao;Wei Yang;Jiahui Yu

{"title":"MSDT: Multiscale Diffusion Transformer for Multimodality Image Fusion","authors":"Caifeng Xia;Hongwei Gao;Wei Yang;Jiahui Yu","doi":"10.1109/TETCI.2025.3542146","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3542146","url":null,"abstract":"Multimodal image fusion is a vital technique that integrates images from various sensors to create a comprehensive and coherent representation, with broad applications in surveillance, medical imaging, and autonomous driving. However, current fusion methods struggle with inadequate feature representation, limited global context understanding due to the small receptive fields of convolutional neural networks (CNNs), and the loss of high-frequency information, all of which lead to suboptimal fusion quality. To address these challenges, we propose the Multi-Scale Diffusion Transformer (MSDT), a novel fusion framework that seamlessly combines a latent diffusion model with a transformer-based architecture. MSDT uses a perceptual compression network to encode source images into a low-dimensional latent space, reducing computational complexity while preserving essential features. It also incorporates a multiscale feature fusion mechanism, enhancing both detail and structural understanding. Additionally, MSDT features a self-attention module to extract unique high-frequency features and a cross-attention module to identify common low-frequency features across modalities, improving contextual understanding. Extensive experiments on three datasets show that MSDT significantly outperforms state-of-the-art methods across twelve evaluation metrics, achieving an SSIM score of 0.98. Moreover, MSDT demonstrates superior robustness and generalizability, highlighting the potential of integrating diffusion models with transformer architectures for multimodal image fusion.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2269-2283"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148165","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

Learning Uniform Latent Representation via Alternating Adversarial Network for Multi-View Clustering 基于交替对抗网络的多视图聚类统一潜在表示学习

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-03-03 DOI: 10.1109/TETCI.2025.3540426

Yue Zhang;Weitian Huang;Xiaoxue Zhang;Sirui Yang;Fa Zhang;Xin Gao;Hongmin Cai

{"title":"Learning Uniform Latent Representation via Alternating Adversarial Network for Multi-View Clustering","authors":"Yue Zhang;Weitian Huang;Xiaoxue Zhang;Sirui Yang;Fa Zhang;Xin Gao;Hongmin Cai","doi":"10.1109/TETCI.2025.3540426","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540426","url":null,"abstract":"Multi-view clustering aims at exploiting complementary information contained in different views to partition samples into distinct categories. The popular approaches either directly integrate features from different views, or capture the common portion between views without closing the heterogeneity gap. Such rigid schemes did not consider the possible mis-alignment among different views, thus failing to learn a consistent yet comprehensive representation, leading to inferior clustering performance. To tackle the drawback, we introduce an alternating adversarial learning strategy to drive different views to fall into the same semantic space. We first present a Linear Alternating Adversarial Multi-view Clustering (Linear-A<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>MC) model to align views in linear embedding spaces. To enjoy the power of feature extraction capability of deep networks, we further build a Deep Alternating Adversarial Multi-view Clustering (Deep-A<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>MC) network to realize non-linear transformations and feature pruning among different views, simultaneously. Specifically, Deep-A<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>MC leverages alternate adversarial learning to first align low-dimensional embedding distributions, followed by a mixture of latent representations synthesized through attention learning for multiple views. Finally, a self-supervised clustering loss is jointly optimized in the unified network to guide the learning of discriminative representations to yield compact clusters. Extensive experiments on six real world datasets with largely varied sample sizes demonstrate that Deep-A<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>MC achieved superior clustering performance by comparing with twelve baseline methods.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2244-2255"},"PeriodicalIF":5.3,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148087","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

Adaptive Feature Transfer for Light Field Super-Resolution With Hybrid Lenses 混合透镜光场超分辨率的自适应特征转移

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-28 DOI: 10.1109/TETCI.2025.3542130

Gaosheng Liu;Huanjing Yue;Xin Luo;Jingyu Yang

{"title":"Adaptive Feature Transfer for Light Field Super-Resolution With Hybrid Lenses","authors":"Gaosheng Liu;Huanjing Yue;Xin Luo;Jingyu Yang","doi":"10.1109/TETCI.2025.3542130","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3542130","url":null,"abstract":"Reconstructing high-resolution (HR) light field (LF) images has shown considerable potential using hybrid lenses—a configuration comprising a central HR sensor and multiple side low-resolution (LR) sensors. Existing methods for super-resolving hybrid lenses LF images typically rely on patch matching or cross-resolution fusion with disparity-based rendering to leverage the high spatial sampling rate of the central view. However, the disparity-resolution gap between the HR central view and the LR side views poses a challenge for local high-frequency transfer. To address this, we introduce a novel framework with an adaptive feature transfer strategy. Specifically, we propose dynamically sampling and aggregating pixels from the HR central feature to effectively transfer high-frequency information to each LR view. The proposed strategy naturally adapts to different disparities and image structures, facilitating information propagation. Additionally, to refine the intermediate LF feature and promote angular consistency, we introduce a spatial-angular cross attention block that enhances domain-specific feature by appropriate weights generated from cross-domain feature. Extensive experimental results demonstrate the superiority of our proposed method over state-of-the-art approaches on both simulated and real-world datasets. The performance gain has significant potential to facilitate the down-stream LF-based applications.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2284-2295"},"PeriodicalIF":5.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148125","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

Memetic Differential Evolution With Adaptive Niching Selection and Diversity-Driven Strategies for Multimodal Optimization 基于自适应生态位选择和多样性驱动策略的模因差分进化多模态优化

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-28 DOI: 10.1109/TETCI.2025.3529903

Yufeng Feng;Weiguo Sheng;Zidong Wang;Gang Xiao;Qi Li;Li Li;Zuling Wang

{"title":"Memetic Differential Evolution With Adaptive Niching Selection and Diversity-Driven Strategies for Multimodal Optimization","authors":"Yufeng Feng;Weiguo Sheng;Zidong Wang;Gang Xiao;Qi Li;Li Li;Zuling Wang","doi":"10.1109/TETCI.2025.3529903","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3529903","url":null,"abstract":"Simultaneously identifying a set of optimal solutions within the landscape of multimodal optimization problem presents a significant challenge. In this work, a differential evolution algorithm with adaptive niching selection, diversity-driven exploration and adaptive local search strategies is proposed to tackle the challenge. In the proposed method, an adaptive niching selection strategy is devised to dynamically select appropriate niching methods from a diverse pool to evolve the population. The pool encompasses niching methods with varying search properties and is dynamically updated during evolution. Further, to enhance exploration, a diversity-driven exploration strategy, which leverages redundant individuals from convergence regions to explore the solution space, is introduced. Additionally, an adaptive local search operation, in which the probability of applying local search and corresponding sampling area are dynamically determined based on the potential of solutions as well as the stage of evolution, is developed to fine-tune promising solutions. The effectiveness of proposed method has been demonstrated on 20 test functions from CEC2013 benchmark suite. Experimental results confirm the effectiveness of our method, demonstrating its superiority compared to related algorithms.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 2","pages":"1322-1339"},"PeriodicalIF":5.3,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716414","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

Solving Multiobjective Combinatorial Optimization via Learning to Improve Method 用学习改进法求解多目标组合优化问题

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-27 DOI: 10.1109/TETCI.2025.3540424

Te Ye;Zizhen Zhang;Qingfu Zhang;Jinbiao Chen;Jiahai Wang

{"title":"Solving Multiobjective Combinatorial Optimization via Learning to Improve Method","authors":"Te Ye;Zizhen Zhang;Qingfu Zhang;Jinbiao Chen;Jiahai Wang","doi":"10.1109/TETCI.2025.3540424","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3540424","url":null,"abstract":"Recently, neural combinatorial optimization (NCO) methods have been prevailing for solving multiobjective combinatorial optimization problems (MOCOPs). Most NCO methods are based on the “Learning to Construct” (L2C) paradigm, where the trained model(s) can directly generate a set of approximate Pareto optimal solutions. However, these methods still suffer from insufficient proximity and poor diversity towards the true Pareto front. In this paper, following the “Learning to Improve” (L2I) paradigm, we propose weight-related policy network (WRPN), a learning-based improvement method for solving MOCOPs. WRPN is incorporated into multiobjective evolutionary algorithm (MOEA) frameworks to effectively guide the search direction. A shared baseline for proximal policy optimization is presented to reduce variance in model training. A quality enhancement mechanism is designed to further refine the Pareto set during model inference. Computational experiments conducted on two classic MOCOPs, i.e., multiobjective traveling salesman problem and multiobjective vehicle routing problem, indicate that our method achieves remarkable results. Notably, our WRPN module can be easily integrated into various MOEA frameworks such as NSGA-II, MOEA/D and MOGLS, providing versatility and applicability across different problem domains.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2122-2136"},"PeriodicalIF":5.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148082","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

MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting MTMD：股票趋势预测的多尺度时间记忆学习和有效去偏框架

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-27 DOI: 10.1109/TETCI.2025.3542107

Mingjie Wang;Juanxi Tian;Mingze Zhang;Jianxiong Guo;Weijia Jia

{"title":"MTMD: Multi-Scale Temporal Memory Learning and Efficient Debiasing Framework for Stock Trend Forecasting","authors":"Mingjie Wang;Juanxi Tian;Mingze Zhang;Jianxiong Guo;Weijia Jia","doi":"10.1109/TETCI.2025.3542107","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3542107","url":null,"abstract":"The endeavor of stock trend forecasting is principally focused on predicting the future trajectory of the stock market, utilizing either manual or technical methodologies to optimize profitability. Recent advancements in machine learning technologies have showcased their efficacy in discerning authentic profit signals within the realm of stock trend forecasting, predominantly employing temporal data derived from historical stock price patterns. Nevertheless, the inherently volatile and dynamic characteristics of the stock market render the learning and capture of multi-scale temporal dependencies and stable trading opportunities a formidable challenge. This predicament is primarily attributed to the difficulty in distinguishing real profit signal patterns amidst a plethora of mixed, noisy data. In response to these complexities, we propose a Multi-Scale Temporal Memory Learning and Efficient Debiasing (MTMD) model. This innovative approach encompasses the creation of a learnable embedding coupled with external attention, serving as a memory module through self-similarity. It aims to mitigate noise interference and bolster temporal consistency within the model. The MTMD model adeptly amalgamates comprehensive local data at each timestamp while concurrently focusing on salient historical patterns on a global scale. Furthermore, the incorporation of a graph network, tailored to assimilate global and local information, facilitates the adaptive fusion of heterogeneous multi-scale data. Rigorous ablation studies and experimental evaluations affirm that the MTMD model surpasses contemporary state-of-the-art methodologies by a substantial margin in benchmark datasets.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2151-2163"},"PeriodicalIF":5.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148152","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

Broad Graph Attention Network With Multiple Kernel Mechanism 具有多核机制的广义图注意网络

IF 5.3 3区计算机科学

IEEE Transactions on Emerging Topics in Computational Intelligence Pub Date : 2025-02-26 DOI: 10.1109/TETCI.2025.3542127

Qingwang Wang;Pengcheng Jin;Hao Xiong;Yuhang Wu;Xu Lin;Tao Shen;Jiangbo Huang;Jun Cheng;Yanfeng Gu

{"title":"Broad Graph Attention Network With Multiple Kernel Mechanism","authors":"Qingwang Wang;Pengcheng Jin;Hao Xiong;Yuhang Wu;Xu Lin;Tao Shen;Jiangbo Huang;Jun Cheng;Yanfeng Gu","doi":"10.1109/TETCI.2025.3542127","DOIUrl":"https://doi.org/10.1109/TETCI.2025.3542127","url":null,"abstract":"Graph neural networks (GNNs) are highly effective models for tasks involving non-Euclidean data. To improve their performance, researchers have explored strategies to increase the depth of GNN structures, as in the case of convolutional neural network (CNN)-based deep networks. However, GNNs relying on information aggregation mechanisms typically face limitations in achieving superior representation performance because of deep feature oversmoothing. Inspired by the broad learning system, in this study, we attempt to avoid the feature oversmoothing issue by expanding the width of GNNs. We propose a broad graph attention network framework with a multikernel mechanism (BGAT-MK). In particular, we propose the construction of a broad GNN using multikernel mapping to generate several reproducing kernel Hilbert spaces (RKHSs), where nodes can wander through different kernel spaces and generate representations. Furthermore, we construct a broader network by aggregating representations in different RKHSs and fusing adaptive weights to aggregate the original and enhanced mapped representations. The efficacy of BGAT-MK is validated through experiments on conventional node classification and light detection and ranging point cloud semantic segmentation tasks, demonstrating its superior performance.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"9 3","pages":"2296-2307"},"PeriodicalIF":5.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144148176","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