Yu, J.,
Tao, D.,
Wang, M.,
Adaptive Hypergraph Learning and its Application in Image
Classification,
IP(21), No. 7, July 2012, pp. 3262-3272.
IEEE DOI
1206
BibRef
Zhang, Z.,
Lin, H.,
Zhao, X.,
Ji, R.,
Gao, Y.,
Inductive Multi-Hypergraph Learning and Its Application on View-Based
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IP(27), No. 12, December 2018, pp. 5957-5968.
IEEE DOI
1810
graph theory, image classification,
learning (artificial intelligence), multi-hypergraph learning.
BibRef
Bonev, B.[Boyan],
Lozano, M.A.[Miguel A.],
Escolano, F.[Francisco],
Suau, P.[Pablo],
Aguilar, W.[Wendy],
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Cazorla, M.A.[Miguel A.],
Region and constellations based categorization of images with
unsupervised graph learning,
IVC(27), No. 7, 4 June 2009, pp. 960-978.
Elsevier DOI
0904
Image categorization; Clustering of graphs; EM algorithms
BibRef
Earlier: A3, A2, A1, A4, A7, A5, Only:
Constellations and the Unsupervised Learning of Graphs,
GbRPR07(340-350).
Springer DOI
0706
BibRef
Romero, A.[Anna],
Cazorla, M.A.[Miguel A.],
Topological SLAM Using Omnidirectional Images:
Merging Feature Detectors and Graph-Matching,
ACIVS10(I: 464-475).
Springer DOI
1012
BibRef
Kang, Z.,
Pan, H.,
Hoi, S.C.H.,
Xu, Z.,
Robust Graph Learning From Noisy Data,
Cyber(50), No. 5, May 2020, pp. 1833-1843.
IEEE DOI
2005
Noise measurement, Adaptation models, Laplace equations, Manifolds,
Task analysis, Reliability, Data models, Clustering,
similarity measure
BibRef
Li, J.N.[Jun-Nan],
Xiong, C.M.[Cai-Ming],
Hoi, S.C.H.[Steven C.H.],
Learning from Noisy Data with Robust Representation Learning,
ICCV21(9465-9474)
IEEE DOI
2203
Representation learning, Codes, Computational modeling,
Benchmark testing, Cleaning, Robustness,
Representation learning
BibRef
Li, J.N.[Jun-Nan],
Wong, Y.K.[Yong-Kang],
Zhao, Q.[Qi],
Kankanhalli, M.S.[Mohan S.],
Learning to Learn From Noisy Labeled Data,
CVPR19(5046-5054).
IEEE DOI
2002
BibRef
Saboksayr, S.S.[Seyed Saman],
Mateos, G.[Gonzalo],
Accelerated Graph Learning From Smooth Signals,
SPLetters(28), 2021, pp. 2192-2196.
IEEE DOI
2112
Signal processing algorithms, Convergence, Topology,
Network topology, Inference algorithms, Convex functions, Tuning,
topology identification
BibRef
Shi, D.[Dan],
Zhu, L.[Lei],
Cheng, Z.Y.[Zhi-Yong],
Li, Z.H.[Zhi-Hui],
Zhang, H.X.[Hua-Xiang],
Unsupervised multi-view feature extraction with dynamic graph
learning,
JVCIR(56), 2018, pp. 256-264.
Elsevier DOI
1811
Multi-view feature extraction, Intrinsic sample relations,
Dynamic graph learning
BibRef
Liang, C.[Cheng],
Wang, L.Z.[Lian-Zhi],
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Zhang, H.X.[Hua-Xiang],
Guo, F.[Fei],
Multi-view unsupervised feature selection with tensor robust
principal component analysis and consensus graph learning,
PR(141), 2023, pp. 109632.
Elsevier DOI
2306
Multi-view unsupervised feature selection,
Low-rank tensor learning, Spectral embedding, Robust sparse regression model
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Zhang, X.[Xiang],
Wang, Q.[Qiao],
Adaptive Online Graph Learning,
SPLetters(32), 2025, pp. 2094-2098.
IEEE DOI
2505
Heuristic algorithms, Signal processing algorithms,
Adaptation models, Vectors, Measurement, Complexity theory, Training,
regret analysis
BibRef
Wang, H.[Hao],
Zhang, S.[Shuo],
Leng, B.[Biao],
HGFormer: Topology-Aware Vision Transformer With HyperGraph Learning,
MultMed(27), 2025, pp. 5746-5757.
IEEE DOI
2509
Topology, Transformers, Visualization, Semantics,
Nearest neighbor methods, Network topology, Data mining, hypergraph learning
BibRef
Yang, X.J.[Xiao-Jun],
Yu, W.Z.[Wei-Zhong],
Wang, R.[Rong],
Zhang, G.H.[Guo-Hao],
Nie, F.P.[Fei-Ping],
Fast spectral clustering learning with hierarchical bipartite graph
for large-scale data,
PRL(130), 2020, pp. 345-352.
Elsevier DOI
2002
Spectral clustering, Hierarchical graph, Bipartite graph,
Large scale data, Out-of-sample
BibRef
Yang, X.J.[Xiao-Jun],
Zheng, Z.H.[Zhen-Hao],
Xie, J.M.[Jie-Ming],
Zhao, W.H.[Wei-Hao],
Xue, J.J.[Jing-Jing],
Nie, F.P.[Fei-Ping],
Spectral ensemble clustering from graph reconstruction with
auto-weighted cluster,
PRL(196), 2025, pp. 243-249.
Elsevier DOI
2509
Spectral clustering, Adaptive weighting, Graph learning
BibRef
Gao, C.H.[Chen-Hui],
Chen, W.Z.[Wen-Zhi],
Nie, F.P.[Fei-Ping],
Yu, W.Z.[Wei-Zhong],
Wang, Z.H.[Zong-Hui],
Spectral clustering with linear embedding: A discrete clustering
method for large-scale data,
PR(151), 2024, pp. 110396.
Elsevier DOI
2404
Spectral clustering, Graph embedding, Unsupervised learning
BibRef
Zhang, Z.T.[Zi-Tong],
Chen, X.J.[Xiao-Jun],
Wang, C.[Chen],
Wang, R.[Ruili],
Song, W.[Wei],
Nie, F.P.[Fei-Ping],
A Structured Bipartite Graph Learning method for ensemble clustering,
PR(160), 2025, pp. 111133.
Elsevier DOI
2501
Clustering, Ensemble clustering, Structure learning
BibRef
Zhao, Z.[Zihua],
Cao, Z.[Zhe],
Xin, H.[Haonan],
Wang, R.[Rong],
Wu, D.Y.[Dan-Yang],
Wang, Z.[Zheng],
Nie, F.P.[Fei-Ping],
Enhancing Clustering Performance With Tensorized High-Order Bipartite
Graphs: A Structured Graph Learning Approach,
CirSysVideo(35), No. 3, March 2025, pp. 2616-2631.
IEEE DOI Code:
WWW Link.
2503
Tensors, Bipartite graph, Noise, Clustering algorithms, Turning,
Sparse matrices, Minimization, Matrix decomposition,
tensor nuclear norm
BibRef
Zhang, H.[Han],
Nie, F.P.[Fei-Ping],
Li, X.L.[Xue-Long],
Large-Scale Clustering With Structured Optimal Bipartite Graph,
PAMI(45), No. 8, August 2023, pp. 9950-9963.
IEEE DOI
2307
Bipartite graph, Scalability, Task analysis, Clustering algorithms,
Optimization, Laplace equations, Partitioning algorithms,
pairwise relation
BibRef
Wang, Z.[Zhen],
Li, Z.Q.[Zhao-Qing],
Wang, R.[Rong],
Nie, F.P.[Fei-Ping],
Li, X.L.[Xue-Long],
Large Graph Clustering With Simultaneous Spectral Embedding and
Discretization,
PAMI(43), No. 12, December 2021, pp. 4426-4440.
IEEE DOI
2112
Clustering methods, Clustering algorithms, Optimization,
Complexity theory, Acceleration, Optical imaging,
label propagation
BibRef
Wen, J.[Jie],
Xu, Y.[Yong],
Liu, H.[Hong],
Incomplete Multiview Spectral Clustering with Adaptive Graph Learning,
Cyber(50), No. 4, April 2020, pp. 1418-1429.
IEEE DOI
2003
Clustering methods, Laplace equations, Cybernetics, Diseases,
Optimization, Clustering algorithms, Matrix decomposition,
low-rank representation
BibRef
Zhang, J.Y.[Ji-Ying],
Li, F.Y.[Fu-Yang],
Xiao, X.[Xi],
Chen, G.Z.[Guan-Zi`],
Xu, T.Y.[Ting-Yang],
Rong, Y.[Yu],
Huang, J.Z.[Jun-Zhou],
Bian, Y.[Yatao],
A Unified Random Walk, Its Induced Laplacians and Spectral
Convolutions for Deep Hypergraph Learning,
PAMI(47), No. 11, November 2025, pp. 10129-10141.
IEEE DOI
2510
Laplace equations, Proteins, Visualization,
Optical wavelength conversion, Data mining, Directed graphs,
equivalence
BibRef
Ma, Y.[Yanni],
Liu, H.[Hao],
Pei, Y.[Yun],
Guo, Y.L.[Yu-Lan],
Heterogeneous Graph Learning for Scene Graph Prediction in 3d Point
Clouds,
ECCV24(XXVI: 274-291).
Springer DOI
2412
BibRef
Koch, S.[Sebastian],
Hermosilla, P.[Pedro],
Vaskevicius, N.[Narunas],
Colosi, M.[Mirco],
Ropinski, T.[Timo],
SGRec3D: Self-Supervised 3D Scene Graph Learning via Object-Level
Scene Reconstruction,
WACV24(3392-3402)
IEEE DOI
2404
Solid modeling, Annotations, Semantics, Predictive models,
Data models, Algorithms, 3D computer vision, Algorithms
BibRef
Chen, J.[Jie],
Li, Z.L.[Zi-Long],
Zhu, Y.[Yin],
Zhang, J.P.[Jun-Ping],
Pu, J.[Jian],
From Node Interaction to Hop Interaction:
New Effective and Scalable Graph Learning Paradigm,
CVPR23(7876-7885)
IEEE DOI
2309
BibRef
Harish, A.N.[Abhinav Narayan],
Nagar, R.[Rajendra],
Raman, S.[Shanmuganathan],
RGL-NET: A Recurrent Graph Learning framework for Progressive Part
Assembly,
WACV22(647-656)
IEEE DOI
2202
Actuators, Shape, Planning, Task analysis, Collision avoidance,
Vision for Robotics
BibRef
Karantaidis, G.[George],
Sarridis, I.[Ioannis],
Kotropoulos, C.[Constantine],
Block Randomized Optimization for Adaptive Hypergraph Learning,
ICIP19(864-868)
IEEE DOI
1910
Adaptive hypergraph learning, Randomized algorithms,
Block randomized singular value decomposition, Conjugate gradient method
BibRef
Chen, W.F.[Wei-Fu],
Feng, G.C.[Guo-Can],
Semi-supervised Graph Learning: Near Strangers or Distant Relatives,
ICPR10(3368-3371).
IEEE DOI
1008
BibRef
Diamond, M.D.,
Narasimhamurthi, N., and
Ganapathy, S.,
A Systematic Approach to Continuous Graph Labeling with
Application to Computer Vision,
AAAI-82(50-54).
BibRef
8200
Tanimoto, S.L., and
Pavlidis, T.,
Graph Labelling Algorithms for Picture Analysis,
ICPR76(749-752).
BibRef
7600
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Network Embedding, Graph Embedding .