Results 41 to 50 of about 6,652,811 (287)

Capped $l_1$ -Norm Sparse Representation Method for Graph Clustering

IEEE Access, 2019
As one of the most popular clustering techniques, graph clustering has attracted many researchers in the field of machine learning and data mining. Generally speaking, graph clustering partitions the data points into different categories according to ...
Mulin Chen, Qi Wang, Shangdong Chen, Xuelong Li   +3 more
doaj   +1 more source

A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection

Remote Sensing, 2022
Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task.
Shu Tian, Lin Cao, Lihong Kang, Xiangwei Xing, Jing Tian, Kangning Du, Ke Sun, Chunzhuo Fan, Yuzhe Fu, Ye Zhang   +9 more
doaj   +1 more source

Temporal Multiresolution Graph Learning

IEEE Access, 2021
Estimating time-varying graphs, i.e., a set of graphs in which one graph represents the relationship among nodes in a certain time slot, from observed data is a crucial problem in signal processing, machine learning, and data mining.
Koki Yamada, Yuichi Tanaka
doaj   +1 more source

Data-Centric Graph Learning: A Survey [PDF]

IEEE Transactions on Big Data, 2023
The history of artificial intelligence (AI) has witnessed the significant impact of high-quality data on various deep learning models, such as ImageNet for AlexNet and ResNet.
Cheng Yang, Deyu Bo, Jixi Liu, Yufei Peng, Boyu Chen, Haoran Dai, Ao Sun, Yue Yu, Yixin Xiao, Qi Zhang, Chuncheng Wang, Yuxin Guo, Chuan Shi   +12 more
semanticscholar   +1 more source

Tensorized Bipartite Graph Learning for Multi-View Clustering [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Despite the impressive clustering performance and efficiency in characterizing both the relationship between the data and cluster structure, most existing graph-based multi-view clustering methods still have the following drawbacks.
Wei Xia, Quanxue Gao, Qianqian Wang, Xinbo Gao, C. Ding, D. Tao   +5 more
semanticscholar   +1 more source

Continual Graph Learning: A Survey [PDF]

arXiv.org, 2023
Research on continual learning (CL) mainly focuses on data represented in the Euclidean space, while research on graph-structured data is scarce. Furthermore, most graph learning models are tailored for static graphs.
Qiao Yuan, S. Guan, Pin Ni, Tianlun Luo, K. Man, Prudence W. H. Wong, Victor I. Chang   +6 more
semanticscholar   +1 more source

Deep Contrastive Graph Learning with Clustering-Oriented Guidance [PDF]

AAAI Conference on Artificial Intelligence
Graph Convolutional Network (GCN) has exhibited remarkable potential in improving graph-based clustering. To handle the general clustering scenario without a prior graph, these models estimate an initial graph beforehand to apply GCN.
Mulin Chen, Bocheng Wang, Xuelong Li
semanticscholar   +1 more source

Graph Learning for Attributed Graph Clustering

Mathematics, 2022
Due to the explosive growth of graph data, attributed graph clustering has received increasing attention recently. Although deep neural networks based graph clustering methods have achieved impressive performance, the huge amount of training parameters make them time-consuming and memory- intensive.
Xiaoran Zhang, Xuanting Xie, Zhao Kang
openaire   +2 more sources

Deep Reinforcement Learning Based on Social Spatial–Temporal Graph Convolution Network for Crowd Navigation

Machines, 2022
In the crowd navigation, reinforcement learning based on graph neural network is a promising method, which effectively solves the poor navigation effect based on social interaction model and the freezing behavior of robot in extreme cases. However, since
Yazhou Lu, Xiaogang Ruan, Jing Huang
doaj   +1 more source

Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization

Frontiers in Big Data
The emergence of advanced artificial intelligence (AI) models has driven the development of frameworks and approaches that focus on automating model training and hyperparameter tuning of end-to-end AI pipelines.
Revathy Venkataramanan, Revathy Venkataramanan, Aalap Tripathy, Tarun Kumar, Sergey Serebryakov, Annmary Justine, Arpit Shah, Suparna Bhattacharya, Martin Foltin, Paolo Faraboschi, Kaushik Roy, Amit Sheth   +11 more
doaj   +1 more source