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graph theory, image classification,
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Image categorization; Clustering of graphs; EM algorithms
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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
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Li, J.N.[Jun-Nan],
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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
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Li, J.N.[Jun-Nan],
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IEEE DOI
2002
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Saboksayr, S.S.[Seyed Saman],
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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
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Shi, D.[Dan],
Zhu, L.[Lei],
Cheng, Z.Y.[Zhi-Yong],
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Unsupervised multi-view feature extraction with dynamic graph
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JVCIR(56), 2018, pp. 256-264.
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Multi-view feature extraction, Intrinsic sample relations,
Dynamic graph learning
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Liang, C.[Cheng],
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2306
Multi-view unsupervised feature selection,
Low-rank tensor learning, Spectral embedding, Robust sparse regression model
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Kang, K.[Kehan],
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Consensus Low-Rank Multi-View Subspace Clustering With Cross-View
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IEEE DOI
2311
BibRef
Chen, Y.Y.[Yong-Yong],
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Zhou, Y.C.[Yi-Cong],
Low-Rank Tensor Graph Learning for Multi-View Subspace Clustering,
CirSysVideo(32), No. 1, January 2022, pp. 92-104.
IEEE DOI
2201
Tensors, Matrix decomposition, Clustering methods,
Adaptation models, Correlation, Clustering algorithms, graph learning
See also Relative Comparison-Based Consensus Learning for Multi-View Subspace Clustering.
BibRef
Wang, S.Q.[Shu-Qin],
Lin, Z.P.[Zhi-Ping],
Cao, Q.[Qi],
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Chen, Y.Y.[Yong-Yong],
Bi-Nuclear Tensor Schatten-p Norm Minimization for Multi-View
Subspace Clustering,
IP(32), 2023, pp. 4059-4072.
IEEE DOI
2307
Tensors, Clustering methods, Correlation, Minimization, Estimation,
Computational complexity, Optimization,
Schatten-p norm
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Wang, X.X.[Xin-Xin],
Zhang, Y.S.[Yong-Shan],
Zhou, Y.C.[Yi-Cong],
Bidirectional Probabilistic Multi-Graph Learning and Decomposition
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IP(34), 2025, pp. 3609-3621.
IEEE DOI Code:
WWW Link.
2507
Tensors, Pipelines, Vectors, Learning systems, Clustering algorithms,
Probabilistic logic, Optimization, Clustering methods, Training,
graph decomposition
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Lv, Z.Y.[Zi-Yu],
Gao, Q.X.[Quan-Xue],
Zhang, X.D.[Xiang-Dong],
Li, Q.[Qin],
Yang, M.[Ming],
View-Consistency Learning for Incomplete Multiview Clustering,
IP(31), 2022, pp. 4790-4802.
IEEE DOI
2208
Tensors, Clustering algorithms, Matrix decomposition, Cameras,
Technological innovation, Representation learning, Optimization,
graph learning
BibRef
Jiang, G.Q.[Guang-Qi],
Peng, J.J.[Jin-Jia],
Wang, H.B.[Hui-Bing],
Mi, Z.[Zetian],
Fu, X.P.[Xian-Ping],
Tensorial Multi-View Clustering via Low-Rank Constrained High-Order
Graph Learning,
CirSysVideo(32), No. 8, August 2022, pp. 5307-5318.
IEEE DOI
2208
Tensors, Correlation, Task analysis, Optimization,
Clustering algorithms, Redundancy, Laplace equations, multi-view clustering
BibRef
Wang, H.B.[Hui-Bing],
Jiang, G.Q.[Guang-Qi],
Peng, J.J.[Jin-Jia],
Deng, R.X.[Ruo-Xi],
Fu, X.P.[Xian-Ping],
Towards Adaptive Consensus Graph: Multi-View Clustering via Graph
Collaboration,
MultMed(25), 2023, pp. 6629-6641.
IEEE DOI
2311
BibRef
Liu, W.Z.[Wen-Zhe],
Jiang, L.[Li],
Wang, H.B.[Hui-Bing],
Zhang, Y.[Yong],
One-step multi-view graph clustering via bottom-up structural
learning,
PR(176), 2026, pp. 113175.
Elsevier DOI
2603
Multi-view learning, Anchor graph, Low-rank tensor, One-step learning
BibRef
Xia, W.[Wei],
Wang, Q.Q.[Qian-Qian],
Gao, Q.X.[Quan-Xue],
Zhang, X.D.[Xiang-Dong],
Gao, X.B.[Xin-Bo],
Self-Supervised Graph Convolutional Network for Multi-View Clustering,
MultMed(24), 2022, pp. 3182-3192.
IEEE DOI
2207
Feature extraction, Decoding, Transforms, Task analysis,
Clustering methods, Social networking (online), Correlation,
self-supervision
BibRef
Shu, X.C.[Xiao-Chuang],
Zhang, X.D.[Xiang-Dong],
Gao, Q.X.[Quan-Xue],
Yang, M.[Ming],
Wang, R.[Rong],
Gao, X.B.[Xin-Bo],
Self-Weighted Anchor Graph Learning for Multi-View Clustering,
MultMed(25), 2023, pp. 5485-5499.
IEEE DOI
2311
BibRef
Li, J.[Jing],
Wang, Q.Q.[Qian-Qian],
Yang, M.[Ming],
Gao, Q.X.[Quan-Xue],
Gao, X.B.[Xin-Bo],
Efficient Anchor Graph Factorization for Multi-View Clustering,
MultMed(26), 2024, pp. 5834-5845.
IEEE DOI
2404
Tensors, Matrix decomposition, Computational complexity,
Data models, Clustering methods, Clustering algorithms,
tensor Schatten p-norm
BibRef
Zhao, W.H.[Wen-Hui],
Li, Q.[Qin],
Xu, H.[Huafu],
Gao, Q.X.[Quan-Xue],
Wang, Q.Q.[Qian-Qian],
Gao, X.B.[Xin-Bo],
Anchor Graph-Based Feature Selection for One-Step Multi-View
Clustering,
MultMed(26), 2024, pp. 7413-7425.
IEEE DOI
2405
Sparse matrices, Feature extraction, Tensors, Clustering methods,
Clustering algorithms, Noise measurement, Sparse approximation,
sparse representation
BibRef
Guo, W.[Wei],
Wang, Z.[Zhe],
Du, W.L.[Wen-Li],
Robust semi-supervised multi-view graph learning with sharable and
individual structure,
PR(140), 2023, pp. 109565.
Elsevier DOI
2305
Semi-supervised learning, Multi-view learning, Clean data, Manifold structure
BibRef
Guo, W.[Wei],
Wang, Z.[Zhe],
Shao, W.[Wei],
Structure Anchor Graph Learning for Multi-View Clustering,
PR(170), 2026, pp. 111880.
Elsevier DOI
2509
Multi-view clustering, Latent space, Anchor graph,
Adaptive neighbors learning, Large-scale dataset
BibRef
Du, S.[Shide],
Cai, Z.L.[Zhi-Ling],
Wu, Z.H.[Zhi-Hao],
Pi, Y.Y.[Yue-Yang],
Wang, S.P.[Shi-Ping],
UMCGL: Universal Multi-View Consensus Graph Learning With Consistency
and Diversity,
IP(33), 2024, pp. 3399-3412.
IEEE DOI
2406
Semantics, Noise, Representation learning, Generators, Training,
Task analysis, Noise measurement, Multi-view learning,
consistency and diversity
BibRef
Fang, Z.[Zihan],
Du, S.[Shide],
Cai, Z.L.[Zhi-Ling],
Lan, S.Y.[Shi-Yang],
Wu, C.M.[Chun-Ming],
Tan, Y.C.[Yan-Chao],
Wang, S.P.[Shi-Ping],
Representation Learning Meets Optimization-Derived Networks:
From Single-View to Multi-View,
MultMed(26), 2024, pp. 8889-8901.
IEEE DOI
2408
To learn extensible models.
Representation learning, Optimization, Task analysis, Training,
Feature extraction, Linear programming, Guidelines,
representation learning
BibRef
Li, L.[Li],
Han, Q.H.[Qi-Hong],
Li, J.Y.[Jia-Yao],
Cui, Z.Q.[Zhan-Qi],
Two-step multi-view data classification based on dynamic Graph-ELM,
PRL(176), 2023, pp. 236-243.
Elsevier DOI
2312
Multi-view data, Classification, Graph learning, GBS
BibRef
Zhang, C.[Chao],
Chen, H.X.[Hao-Xing],
Li, H.X.[Hua-Xiong],
Chen, C.L.[Chun-Lin],
Learning latent disentangled embeddings and graphs for multi-view
clustering,
PR(156), 2024, pp. 110839.
Elsevier DOI
2408
Multi-view clustering, Embedding disentanglement,
Graph learning, Low-rank tensor
BibRef
Xu, D.[Deng],
Zhang, C.[Chao],
Li, Z.C.[Ze-Chao],
Chen, C.L.[Chun-Lin],
Li, H.X.[Hua-Xiong],
Fast Disentangled Slim Tensor Learning for Multi-View Clustering,
MultMed(27), 2025, pp. 1254-1265.
IEEE DOI
2503
Tensors, Correlation, Semantics, Vectors, Static VAr compensators,
Stacking, Principal component analysis, Fast Fourier transforms,
slim tensor learning
BibRef
Wan, M.H.[Ming-Hua],
Zhu, J.Y.[Jing-Yu],
Sun, C.L.[Chang-Le],
Yang, Z.J.[Zhang-Jing],
Yin, J.[Jun],
Yang, G.[Guowei],
Tensor Low-Rank Graph Embedding and Learning for One-Step Incomplete
Multi-View Clustering,
MultMed(26), 2024, pp. 9763-9775.
IEEE DOI
2410
Tensors, Clustering algorithms, Clustering methods, Correlation,
Computer science, Symbols, Sun, Graph embedding,
tensor low-rank constraint
BibRef
Sun, W.J.[Wei-Jun],
Li, C.[Chaoye],
Li, Q.Y.[Qiao-Yun],
Fang, X.Z.[Xiao-Zhao],
He, J.[Jiakai],
Liu, L.[Lei],
Joint Intra-view and Inter-view Enhanced Tensor Low-rank Induced
Affinity Graph Learning,
PR(159), 2025, pp. 111140.
Elsevier DOI
2412
Multi-view clustering, Graph learning, Tensor, Low-rank
BibRef
Wu, S.S.[Shan-Shan],
Lu, G.F.[Gui-Fu],
Consensus Incomplete Multi-View Clustering with Nonconvex Tensor
Smooth Graph Learning,
PR(167), 2025, pp. 111764.
Elsevier DOI Code:
WWW Link.
2506
Incomplete multi-view clustering, global low-rank,
local smooth, nonconvex tensor approximation
BibRef
Wang, F.[Fei],
Lu, G.F.[Gui-Fu],
Tensorized Pure Nonlinear Anchor Graph Learning for Multi-View
Clustering,
PR(179), 2026, pp. 113876.
Elsevier DOI Code:
WWW Link.
2606
Multi-view clustering, Nonlinear learning, Anchor graph, Tensor constraint
See also Efficient and Robust MultiView Clustering with Anchor Graph Regularization.
See also Joint Learning of Latent Subspace and Structured Graph for Multi-View Clustering.
See also One-Step Multi-View Clustering With Adaptive Low-Rank Anchor-Graph Learning.
BibRef
Kong, Z.[Zisen],
Fu, Z.Q.[Zhi-Qiang],
Chang, D.X.[Dong-Xia],
Wang, Y.M.[Yi-Ming],
Zhao, Y.[Yao],
Dual-space Co-training for Large-scale Multi-view Clustering,
PR(168), 2025, pp. 111844.
Elsevier DOI
2506
Co-training, Multi-view Clustering, Anchor graph learning,
Large-scale clustering, Dual-space
BibRef
Cui, J.R.[Jin-Rong],
Li, Y.T.[Yu-Ting],
Huang, H.[Han],
Wen, J.[Jie],
Dual Contrast-Driven Deep Multi-View Clustering,
IP(33), 2024, pp. 4753-4764.
IEEE DOI Code:
WWW Link.
2409
Feature extraction, Contrastive learning, Reliability,
Clustering methods, Task analysis, Data mining, contrastive learning
BibRef
Fei, L.K.[Lun-Ke],
He, J.L.[Jun-Lin],
Zhu, Q.[Qi],
Zhao, S.P.[Shu-Ping],
Wen, J.[Jie],
Xu, Y.[Yong],
Deep Multi-View Contrastive Clustering via Graph Structure Awareness,
IP(34), 2025, pp. 3805-3816.
IEEE DOI
2507
Contrastive learning, Feature extraction, Autoencoders,
Data mining, Clustering methods, Training, Reliability, self-supervision
BibRef
Zhao, S.P.[Shu-Ping],
Fei, L.K.[Lun-Ke],
Wen, J.[Jie],
Wu, J.G.[Ji-Gang],
Zhang, B.[Bob],
Intrinsic and Complete Structure Learning Based Incomplete Multiview
Clustering,
MultMed(25), 2023, pp. 1098-1110.
IEEE DOI
2305
Sparse matrices, Representation learning, Optimization, Clustering methods,
Task analysis, Correlation, Indexes, intrinsic structure learning
BibRef
Wong, W.K.[Wai Keung],
Han, N.[Na],
Fang, X.Z.[Xiao-Zhao],
Zhan, S.H.[Shan-Hua],
Wen, J.[Jie],
Clustering Structure-Induced Robust Multi-View Graph Recovery,
CirSysVideo(30), No. 10, October 2020, pp. 3584-3597.
IEEE DOI
2010
Sparse matrices, Learning systems, Optimization, Task analysis,
Laplace equations, Clustering algorithms, Noise measurement,
alternating optimization
BibRef
Wang, Y.M.[Yi-Ming],
Chang, D.X.[Dong-Xia],
Fu, Z.Q.[Zhi-Qiang],
Wen, J.[Jie],
Zhao, Y.[Yao],
Graph Contrastive Partial Multi-View Clustering,
MultMed(25), 2023, pp. 6551-6562.
IEEE DOI
2311
BibRef
Wen, J.[Jie],
Liu, C.L.[Cheng-Liang],
Xu, G.[Gehui],
Wu, Z.H.[Zhi-Hao],
Huang, C.[Chao],
Fei, L.K.[Lun-Ke],
Xu, Y.[Yong],
Highly Confident Local Structure Based Consensus Graph Learning for
Incomplete Multi-View Clustering,
CVPR23(15712-15721)
IEEE DOI
2309
BibRef
Liu, J.L.[Jian-Lun],
Teng, S.H.[Shao-Hua],
Fei, L.K.[Lun-Ke],
Zhang, W.[Wei],
Fang, X.Z.[Xiao-Zhao],
Zhang, Z.X.[Zhu-Xiu],
Wu, N.Q.[Nai-Qi],
A Novel Consensus Learning Approach to Incomplete Multi-View
Clustering,
PR(115), 2021, pp. 107890.
Elsevier DOI
2104
Multi-view clustering, Incomplete multi-view clustering,
Consensus representation, Consensus similarity graph
BibRef
Li, Z.L.[Zheng-Lai],
Tang, C.[Chang],
Liu, X.W.[Xin-Wang],
Zheng, X.[Xiao],
Zhang, W.[Wei],
Zhu, E.[En],
Consensus Graph Learning for Multi-View Clustering,
MultMed(24), 2022, pp. 2461-2472.
IEEE DOI
2205
Tensors, Clustering methods, Optimization, Streaming media, Feature extraction,
Task analysis, Correlation, weighted tensor nuclear norm
See also Align Then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.
BibRef
Sun, L.[Lilei],
Wong, W.K.[Wai Keung],
Fu, Y.[Yusen],
Wen, J.[Jie],
Li, M.[Mu],
Lu, Y.[Yuwu],
Fei, L.K.[Lun-Ke],
Dual Structure-Aware Consensus Graph Learning for Incomplete
Multi-View Clustering,
PR(165), 2025, pp. 111582.
Elsevier DOI
2505
Graph learning, Incomplete multi-view clustering, Missing views
BibRef
Wu, H.J.[Hong-Jie],
Huang, S.D.[Shu-Dong],
Tang, C.W.[Chen-Wei],
Zhang, Y.C.[Yan-Cheng],
Lv, J.C.[Jian-Cheng],
Pure graph-guided multi-view subspace clustering,
PR(136), 2023, pp. 109187.
Elsevier DOI
2301
Multi-view learning, Subspace clustering, Graph learning, Pure graph
BibRef
Wang, Y.N.[Yi-Nuo],
Guo, Y.[Yu],
Wang, Z.[Zheng],
Wang, F.[Fei],
Joint Learning of Latent Subspace and Structured Graph for Multi-View
Clustering,
PR(154), 2024, pp. 110592.
Elsevier DOI
2406
Multi-view clustering, Latent space, Subspace clustering, Graph learning
See also Tensorized Pure Nonlinear Anchor Graph Learning for Multi-View Clustering.
BibRef
Wang, X.L.[Xiao-Li],
Zhu, Z.F.[Zhi-Fan],
Song, Y.[Yan],
Fu, H.J.[Hai-Juan],
GRNet: Graph-based remodeling network for multi-view semi-supervised
classification,
PRL(151), 2021, pp. 95-102.
Elsevier DOI
2110
Multi-view semi-supervised learning, Remodeling network,
Consistency and complementation
BibRef
Wang, X.L.[Xiao-Li],
Fu, L.Y.[Li-Yong],
Zhang, Y.D.[Yu-Dong],
Wang, Y.L.[Yong-Li],
Li, Z.C.[Ze-Chao],
MMatch: Semi-Supervised Discriminative Representation Learning for
Multi-View Classification,
CirSysVideo(32), No. 9, September 2022, pp. 6425-6436.
IEEE DOI
2209
Representation learning, Training, Predictive models, Forestry,
Feature extraction, Entropy, Task analysis, pseudo-labeling
BibRef
Wang, X.L.[Xiao-Li],
Wang, Y.L.[Yong-Li],
Wang, Y.P.[Yu-Peng],
Huang, A.[Anqi],
Liu, J.[Jun],
Trusted Semi-Supervised Multi-View Classification with Contrastive
Learning,
MultMed(26), 2024, pp. 8268-8278.
IEEE DOI
2408
Uncertainty, Semantics, Self-supervised learning, Deep learning,
Ensemble learning, Ions, Estimation, Semi-supervised learning,
uncertainty estimation
BibRef
Jiang, Y.[Yu],
Liu, J.[Jing],
Li, Z.C.[Ze-Chao],
Lu, H.Q.[Han-Qing],
Semi-Supervised Unified Latent Factor Learning with Multi-View Data,
MVA(25), No. 7, October 2014, pp. 1635-1645.
Springer DOI
1410
BibRef
Jiang, B.B.[Bing-Bing],
Liu, J.[Jun],
Wang, Z.D.[Zi-Dong],
Zhang, C.L.[Cheng-Long],
Yang, J.[Jie],
Wang, Y.[Yadi],
Sheng, W.G.[Wei-Guo],
Ding, W.P.[Wei-Ping],
Semi-Supervised Multi-View Feature Selection with Adaptive Similarity
Fusion and Learning,
PR(159), 2025, pp. 111159.
Elsevier DOI
2412
Multi-view feature selection, Semi-supervised learning,
Similarity fusion, Graph learning
BibRef
Liang, K.[Ke],
Meng, L.Y.[Ling-Yuan],
Li, H.[Hao],
Wang, J.[Jun],
Lan, L.[Long],
Li, M.M.[Miao-Miao],
Liu, X.W.[Xin-Wang],
Wang, H.[Huaimin],
From Concrete to Abstract: Multi-View Clustering on Relational
Knowledge,
PAMI(47), No. 10, October 2025, pp. 9043-9060.
IEEE DOI
2510
Correlation, Marine animals, Knowledge graphs, Feature extraction,
Vectors, Data mining, Visualization, Training, Information retrieval,
relational knowledge
BibRef
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
Zhao, X.W.[Xiao-Wei],
Xie, L.[Linrui],
Chang, X.J.[Xiao-Jun],
Nie, F.P.[Fei-Ping],
Zhang, Q.[Qiang],
FC2: Fast Co-Clustering With Small-Scale Similarity Graph and
Bipartite Graph Learning,
PAMI(48), No. 6, June 2026, pp. 6570-6586.
IEEE DOI
2605
Bipartite graph, Matrix decomposition, Manifolds, Sparse matrices,
Correlation, Clustering algorithms, Artificial intelligence,
balanced cluster assignments
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.N.[Hao-Nan],
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
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Incomplete Multiview Spectral Clustering with Adaptive Graph Learning,
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IEEE DOI
2003
Clustering methods, Laplace equations, Cybernetics, Diseases,
Optimization, Clustering algorithms, Matrix decomposition,
low-rank representation
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Huang, J.Z.[Jun-Zhou],
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A Unified Random Walk, Its Induced Laplacians and Spectral
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PAMI(47), No. 11, November 2025, pp. 10129-10141.
IEEE DOI
2510
Laplace equations, Proteins, Visualization,
Optical wavelength conversion, Data mining, Directed graphs,
equivalence
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Zhang, L.[Li],
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PR(177), 2026, pp. 113328.
Elsevier DOI
2605
Graph neural networks, Contrastive learning,
Heterophilic graph, Node classification
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Tang, C.[Chuan],
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Wang, J.[Jun],
Tang, C.[Chang],
Zhu, E.[En],
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Sample-Level Weighted and Structure-Enhanced Anchor Graph Learning
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CirSysVideo(36), No. 5, May 2026, pp. 7382-7394.
IEEE DOI Code:
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2605
Optimization, Videos, Convergence, Clustering methods, Adaptation models,
Vectors, Technological innovation, Symbols, efficient clustering
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Tang, C.[Chuan],
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Wang, J.[Jun],
Guan, R.X.[Ren-Xiang],
Wang, S.W.[Si-Wei],
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Zhu, E.[En],
Liu, X.W.[Xin-Wang],
Align Then Tensorize: Multi-Level Consistent Anchor Graph Learning
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IP(35), 2026, pp. 7049-7062.
IEEE DOI Code:
WWW Link.
2607
Tensors, Learning (artificial intelligence),
Ranking (statistics), Modeling, Matrices, Optimization, Machining,
tensor low-rank representation
See also Consensus Graph Learning for Multi-View Clustering.
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Shi, L.[Long],
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Tensor-Based Graph Learning With Consistency and Specificity for
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MultMed(28), 2026, pp. 1562-1575.
IEEE DOI
2603
Tensors, Noise, Manifolds, Vectors, Streaming media, Data mining, Finance,
Fast Fourier transforms, Euclidean distance, Consistency, specificity
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Wang, B.C.[Bo-Cheng],
Wang, J.W.[Jia-Wei],
Zeng, C.S.[Chu-Sheng],
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Locality-driven flexible consensus graph learning for multi-view
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PR(169), 2026, pp. 111853.
Elsevier DOI
2509
Internal cluster validity index, Noise suppression,
Graph convolutional network, Deep graph learning, Multi-view clustering
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Guo, J.P.[Ji-Peng],
Xu, X.[Xiang],
Cao, Y.[Yu],
Cao, M.[Man],
Xin, M.Y.[Meng-Yuan],
Zhao, T.X.[Tian-Xiang],
Su, Y.[Ye],
Gao, J.B.[Jun-Bin],
Cui, M.L.[Ming-Liang],
Wang, Y.Q.[You-Qing],
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PR(177), 2026, pp. 113378.
Elsevier DOI Code:
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2605
Multi-view clustering, Scalable graph learning,
Anchor-to-graph structural co-regularization
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Liu, M.[Meng],
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Li, M.M.[Miao-Miao],
Zhu, X.L.[Xue-Ling],
Liu, X.W.[Xin-Wang],
Dictionary Multi-Modal Temporal Graph Learning,
PAMI(48), No. 8, August 2026, pp. 9606-9623.
IEEE DOI
2607
Motion pictures, Frequency modulation,
Broadcasting, Central Processing Unit,
temporal graph
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Li, W.R.[Wen-Rui],
Zhang, Q.H.[Qing-Hao],
Wang, X.[Xiaowo],
Mechanisms Under Shifts: Interpretable Clustering With Self-Improving
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PAMI(48), No. 8, August 2026, pp. 10083-10096.
IEEE DOI
2607
Receivers, Transmitters, Wireless Access in Vehicular Environments,
self-improving learning
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Huang, Y.[Yinan],
Miao, S.Q.[Si-Qi],
Li, P.[Pan],
How Can State Space Models Enhance Machine Learning on Graphs?,
PAMI(48), No. 8, August 2026, pp. 10184-10191.
IEEE DOI
2607
Broadcasting, Broadcast technology, Central Processing Unit,
Filtering, Filters, LoRa, Data communication, state space models
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Ma, Y.[Yanni],
Liu, H.[Hao],
Pei, Y.[Yun],
Guo, Y.L.[Yu-Lan],
Heterogeneous Graph Learning for Scene Graph Prediction in 3d Point
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ECCV24(XXVI: 274-291).
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2412
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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
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WACV24(3392-3402)
IEEE DOI
2404
Solid modeling, Annotations, Semantics, Predictive models,
Data models, Algorithms, 3D computer vision, Algorithms
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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
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Harish, A.N.[Abhinav Narayan],
Nagar, R.[Rajendra],
Raman, S.[Shanmuganathan],
RGL-NET: A Recurrent Graph Learning framework for Progressive Part
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WACV22(647-656)
IEEE DOI
2202
Actuators, Shape, Planning, Task analysis, Collision avoidance,
Vision for Robotics
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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
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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).
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8200
Tanimoto, S.L., and
Pavlidis, T.,
Graph Labelling Algorithms for Picture Analysis,
ICPR76(749-752).
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7600
Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Network Embedding, Graph Embedding .