Collections for state-of-the-art and novel deep neural network-based multi-view clustering approaches (papers & codes). According to the integrity of multi-view data, such methods can be further subdivided into Deep Multi-view Clustering(DMVC) and Deep Incomplete Multi-view Clustering(DIMVC).
We are looking forward for other participants to share their papers and codes. If interested or any question about the listed papers and codes, please contact [email protected]. If you find this repository useful to your research or work, it is really appreciated to star this repository. ✨ If you use our code or the processed datasets in this repository for your research, please cite 1-2 papers in the citation part here. ❤️
Deep multi-view clustering aims to reveal the potential complementary information of multiple features or modalities through deep neural networks, and finally divide samples into different groups in unsupervised scenarios.
| Year | Title | Venue | Paper |
|---|---|---|---|
| 2024 | A Survey and an Empirical Evaluation of Multi-view Clustering Approaches | ACM CS | |
| 2024 | Self‐Supervised Multi‐View Clustering in Computer Vision: A Survey | IET CV | |
| 2024 | The Methods for Improving Large-Scale Multi-View Clustering Efficiency: A Survey | AIR | |
| 2024 | Deep Clustering：A Comprehensive Survey | TNNLS | |
| 2024 | Breaking Down Multi-view Clustering：A Comprehensive Review of Multi-view Approaches for Complex Data Structures | EAAI | |
| 2024 | Incomplete Multi-view Learning: Review, Analysis, and Prospects | ASC | |
| 2023 | A Comprehensive Survey on Multi-view Clustering | TKDE | |
| 2022 | Representation Learning in Multi-view Clustering: A Literature Review | DSE | |
| 2022 | Foundations and Recent Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions | Arxiv | |
| 2021 | Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry, and Fusion | TOMM | |
| 2021 | Deep Multi-view Learning Methods: A Review | Neurocom | |
| 2018 | A Survey of Multi-View Representation Learning | TKDE | |
| 2018 | Multi-view Clustering: A Survey | BDMA | |
| 2018 | Multimodal Machine Learning: A Survey and Taxonomy | TPAMI | |
| 2018 | A Survey on Multi-View Clustering | Arxiv | |
| 2017 | Multi-view Learning Overview：Recent Progress and New Challenges | IF | |
| 2013 | A Survey on Multi-view Learning | Arxiv |
According to the integrity of multi-view data, the paper is divided into deep multi-view clustering methods and deep incomplete multi-view clustering approaches.
| Year | Title | Abbreviation | Venue | Paper | Code |
|---|---|---|---|---|---|
| 2024 | Adversarially Robust Deep Multi-View Clustering: A Novel Attack and Defense Framework | AR-DMVC-AM | ICML | ||
| 2024 | Bridging Gaps: Federated Multi-View Clustering in Heterogeneous Hybrid Views | FMCSC | NeurIPS | ||
| 2024 | Robust Contrastive Multi-view Clustering against Dual Noisy Correspondence | CANDY | NeurIPS | ||
| 2024 | Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering | SCE-MVC | NeurIPS | - | |
| 2024 | Investigating and Mitigating the Side Effects of Noisy Views for Self-Supervised Clustering Algorithms in Practical Multi-View Scenarios | MVCAN | CVPR | ||
| 2024 | Rethinking Multi-view Representation Learning via Distilled Disentangling | MRDD | CVPR | ||
| 2024 | Differentiable Information Bottleneck for Deterministic Multi-view Clustering | DIB | CVPR | - | |
| 2024 | Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders | CMVAE | ICLR | - | |
| 2024 | Learning Common Semantics via Optimal Transport for Contrastive Multi-view Clustering | CSOT | TIP | ||
| 2024 | Dual Contrast-Driven Deep Multi-View Clustering | DCMVC | TIP | ||
| 2024 | Multiview Deep Subspace Clustering Networks | MvDSCN | TCYB | - | |
| 2024 | Deep Contrastive Multi-View Subspace Clustering With Representation and Cluster Interactive Learning | DCMVSC | TKDE | - | |
| 2024 | Robust Multi-View Clustering with Noisy Correspondence | RMCNC | TKDE | ||
| 2024 | Integrating Vision-Language Semantic Graphs in Multi-View Clustering | IVSGMV | IJCAI | - | |
| 2024 | Simple Contrastive Multi-View Clustering with Data-Level Fusion | SCM | IJCAI | ||
| 2024 | Dynamic Weighted Graph Fusion for Deep Multi-View Clustering | DFMVC | IJCAI | - | |
| 2024 | Contrastive and View-Interaction Structure Learning for Multi-view Clustering | SERIES | IJCAI | - | |
| 2024 | Active Deep Multi-view Clustering | ADMC | IJCAI | ||
| 2024 | Homophily-Related: Adaptive Hybrid Graph Filter for Multi-View Graph Clustering | AHGFC | AAAI | - | |
| 2024 | SURER: Structure-Adaptive Unified Graph Neural Network for Multi-View Clustering | SURER | AAAI | ||
| 2024 | Heterogeneity-Aware Federated Deep Multi-View Clustering towards Diverse Feature Representations | HFMVC | ACM MM | ||
| 2024 | EMVCC: Enhanced Multi-View Contrastive Clustering for Hyperspectral Images | EMVCC | ACM MM | ||
| 2024 | DFMVC: Deep Fair Multi-view Clustering | DFMVC | ACM MM | - | |
| 2024 | View Gap Matters: Cross-view Topology and Information Decoupling for Multi-view Clustering | TGM-MVC | ACM MM | - | |
| 2024 | Learning Dual Enhanced Representation for Contrastive Multi-view Clustering | LUCE-CMC | ACM MM | ||
| 2024 | Contrastive Graph Distribution Alignment for Partially View-Aligned Clustering | CGDA | ACM MM | - | |
| 2024 | Dual-Optimized Adaptive Graph Reconstruction for Multi-View Graph Clustering | DOAGC | ACM MM | - | |
| 2024 | Robust Variational Contrastive Learning for Partially View-unaligned Clustering | VITAL | ACM MM | ||
| 2024 | Self-Weighted Contrastive Fusion for Deep Multi-View Clustering | SCMVC | TMM | ||
| 2024 | Subspace-Contrastive Multi-View Clustering | SCMC | TKDD | - | |
| 2024 | Multi-view contrastive clustering via integrating graph aggregation and confidence enhancement | MAGA | IF | ||
| 2024 | Trustworthy multi-view clustering via alternating generative adversarial representation learning and fusion | AGARL | IF | - | |
| 2024 | Structural deep multi-view clustering with integrated abstraction and detail | SMVC | NN | - | |
| 2024 | Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-View Clustering | PNCL-FDMC | NN | - | |
| 2024 | Composite Attention Mechanism Network for Deep Contrastive Multi-view Clustering | CAMVC | NN | - | |
| 2024 | Asymmetric Double-Winged Multi-View Clustering Network for Exploring Diverse and Consistent Information | CodingNet | NN | - | |
| 2024 | Decomposed deep multi-view subspace clustering with self-labeling supervision | D2MVSC | IS | - | |
| 2024 | Structure-guided feature and cluster contrastive learning for multi-view clustering | SGFCC | Neurcom | - | |
| 2024 | Learning consensus representations in multi-latent spaces for multi-view clustering | DMCC | Neurcom | - | |
| 2024 | MCoCo: Multi-level Consistency Collaborative Multi-view Clustering | MCoCo | ESA | - | |
| 2024 | Graph-Driven Deep Multi-View Clustering with Self-Paced Learning | GDMVC | KBS | ||
| 2024 | Information Bottleneck Fusion for Deep Multi-view Clustering | IBFDMVC | KBS | - | |
| 2024 | Separable Consistency and Diversity Feature Learning for Multi-View Clustering | SCDFL | SPL | - | |
| 2023 | Graph Embedding Contrastive Multi-Modal Representation Learning for Clustering | GECMC | TIP | ||
| 2023 | Neighbor-aware deep multi-view clustering via graph convolutional network | NMvC-GCN | IF | ||
| 2023 | Joint contrastive triple-learning for deep multi-view clustering | JCT | IPM | ||
| 2023 | Auto-attention mechanism for multi-view deep embedding clustering | MDEC | PR | - | |
| 2023 | Deep multi-view spectral clustering via ensemble | DMCE | PR | - | |
| 2023 | Unified Representation Learning for Multi-View Clustering by Between/Within View Deep Majorization | deepURL | TETCI | ||
| 2023 | Dropping pathways towards deep multi-view graph subspace clustering networks | DPMGSC | ACM MM | - | |
| 2023 | Triple-granularity contrastive learning for deep multi-view subspace clustering | TRUST | ACM MM | - | |
| 2023 | Deep multiview adaptive clustering with semantic invariance | DMAC-SI | TNNLS | ||
| 2023 | Generalized Information-theoretic Multi-view Clustering | IMC | NeurIPS | - | |
| 2023 | Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration | SEM | NeurIPS | ||
| 2023 | A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective | SUMVC | NeurIPS | ||
| 2023 | Dual Label-Guided Graph Refnement for Multi-View Graph Clustering | DuaLGR | AAAI | ||
| 2023 | Cross-view Topology Based Consistent and Complementary Information for Deep Multi-view Clustering | CTCC | ICCV | - | |
| 2023 | MHCN: A Hyperbolic Neural Network Model for Multi-view Hierarchical Clustering | MHCN | ICCV | - | |
| 2023 | Deep Multiview Clustering by Contrasting Cluster Assignments | CVCL | ICCV | ||
| 2023 | DealMVC: Dual Contrastive Calibration for Multi-view Clustering | DealMVC | ACM MM | ||
| 2023 | Self-Supervised Graph Attention Networks for Deep Weighted Multi-View Clustering | SGDMC | AAAI | - | |
| 2023 | Dual Fusion-Propagation Graph Neural Network for Multi-view Clustering | DFP-GNN | TMM | - | |
| 2023 | Joint Shared-and-Specific Information for Deep Multi-View Clustering | JSSI | TCSVT | - | |
| 2023 | On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering | DeepMVC | CVPR | ||
| 2023 | GCFAgg：Global and Cross-view Feature Aggregation for Multi-view Clustering | GCFAgg | CVPR | - | |
| 2023 | Self-Supervised Information Bottleneck for Deep Multi-View Subspace Clustering | SIB-MSC | TIP | - | |
| 2023 | Multi-channel Augmented Graph Embedding Convolutional Network for Multi-view Clustering | MAGEC-Net | TNSE | - | |
| 2022 | Deep Safe Multi-View Clustering：Reducing the Risk of Clustering Performance Degradation Caused by View Increase | DSMVC | CVPR | ||
| 2022 | Multi-level Feature Learning for Contrastive Multi-view Clustering | MFLVC | CVPR | ||
| 2022 | Stationary Diffusion State Neural Estimation for Multiview Clustering | SDSNE | AAAI | ||
| 2022 | Multi-View Subspace Clustering via Structured Multi-Pathway Network | SMpNet | TNNLS | ||
| 2022 | Multiview Subspace Clustering With Multilevel Representations and Adversarial Regularization | MvSC-MRAR | TNNLS | - | |
| 2022 | Self-Supervised Deep Multiview Spectral Clustering | SDMvSC | TNNLS | - | |
| 2022 | Contrastive Multi-view Hyperbolic Hierarchical Clustering | CMHHC | IJCAI | - | |
| 2022 | Multi-view Graph Embedding Clustering Network：Joint Self-supervision and Block Diagonal Representation | MVGC | NN | ||
| 2022 | Efficient Multi‑view Clustering Networks | EMC-Nets | APPL INTELL | ||
| 2021 | Deep Mutual Information Maximin for Cross-Modal Clustering | DMIM | AAAI | - | |
| 2021 | Uncertainty-Aware Multi-View Representation Learning | DUA-Nets | AAAI | ||
| 2021 | Learning Deep Sparse Regularizers With Applications to Multi-View Clustering and Semi-Supervised Classification | DSRL | TPAMI | ||
| 2021 | Reconsidering Representation Alignment for Multi-view Clustering | SiMVC&CoMVC | CVPR | ||
| 2021 | Deep Multiple Auto-Encoder-Based Multi-view Clustering | MVC_MAE | DSE | ||
| 2021 | Multimodal Clustering Networks for Self-supervised Learning from Unlabeled Videos | MCN | ICCV | ||
| 2021 | Multi-VAE: Learning Disentangled View-common and View-peculiar Visual Representations for Multi-view Clustering | Multi-VAE | ICCV | ||
| 2021 | Graph Filter-based Multi-view Attributed Graph Clustering | MvAGC | IJCAI | ||
| 2021 | Multi-view Subspace Clustering Networks with Local and Global Graph Information | MSCNGL | Neurocom | ||
| 2021 | Attentive Multi-View Deep Subspace Clustering Net | AMVDSN | Neurocom | - | |
| 2021 | Multi-view Contrastive Graph Clustering | MCGC | NeurIPS | ||
| 2021 | Self-supervised Discriminative Feature Learning for Deep Multi-view Clustering | SDMVC | TKDE | ||
| 2021 | Multi-view Attributed Graph Clustering | MAGC | TKDE | ||
| 2021 | Deep Multi-view Subspace Clustering with Unified and Discriminative Learning | DMSC-UDL | TMM | ||
| 2021 | Self-supervised Graph Convolutional Network for Multi-view Clustering | SGCMC | TMM | ||
| 2021 | Consistent Multiple Graph Embedding for Multi-View Clustering | CMGEC | TMM | ||
| 2021 | Deep Multiview Collaborative Clustering | DMCC | TNNLS | - | |
| 2020 | Partially View-aligned Clustering | PVC | NeurIPS | ||
| 2020 | Cross-modal Subspace Clustering via Deep Canonical Correlation Analysis | CMSC-DCCA | AAAI | - | |
| 2020 | Shared Generative Latent Representation Learning for Multi-View Clustering | DMVCVAE | AAAI | ||
| 2020 | End-to-End Adversarial-Attention Network for Multi-Modal Clustering | EAMC | CVPR | ||
| 2020 | Multi-View Attribute Graph Convolution Networks for Clustering | MAGCN | IJCAI | ||
| 2020 | End-To-End Deep Multimodal Clustering | DMMC | ICME | ||
| 2020 | Deep Embedded Multi-view Clustering with Collaborative Training | DEMVC | IS | ||
| 2020 | Joint Deep Multi-View Learning for Image Clustering | DMJC | TKDE | - | |
| 2020 | One2Multi Graph Autoencoder for Multi-view Graph Clustering | O2MVC | WWW | ||
| 2019 | AE^2-Nets: Autoencoder in Autoencoder Networks | AE^2-Nets | CVPR | ||
| 2019 | COMIC: Multi-view Clustering Without Parameter Selection | COMIC | ICML | ||
| 2019 | Deep Adversarial Multi-view Clustering Network | DAMC | IJCAI | ||
| 2019 | Multi-view Spectral Clustering Network | MvSCN | IJCAI | ||
| 2019 | Multi-view Deep Subspace Clustering Networks | MvDSCN | TIP | ||
| 2018 | Generalized Latent Multi-View Subspace Clustering | gLMSC | TPAMI | ||
| 2018 | Deep Multimodal Subspace Clustering Networks | DMSC | STSP | ||
| 2018 | Deep Multi-View Clustering via Multiple Embedding | DMVC-ME | CoRR | - |
| Year | Title | Abbreviation | Venue | Paper | Code |
|---|---|---|---|---|---|
| 2024 | Diffusion-based Missing-view Generation With the Application on Incomplete Multi-view Clustering | DMVG | ICML | ||
| 2024 | Deep Variational Incomplete Multi-View Clustering: Exploring Shared Clustering Structures | DVIMC | AAAI | ||
| 2024 | Partial Multi-View Clustering via Self-Supervised Network | PVC-SCN | AAAI | - | |
| 2024 | Incomplete Contrastive Multi-View Clustering with High-Confidence Guiding | ICMVC | AAAI | ||
| 2024 | Adaptive Feature Imputation with Latent Graph for Deep Incomplete Multi-View Clustering | AGDIMC | AAAI | - | |
| 2024 | Decoupled Contrastive Multi-view Clustering with High-order Random Walks | DIVIDE | AAAI | ||
| 2024 | Robust Prototype Completion for Incomplete Multi-view Clustering | RPCIC | ACM MM | ||
| 2024 | URRL-IMVC: Unified and Robust Representation Learning for Incomplete Multi-View Clustering | URRL-IMVC | SIGKDD | - | |
| 2024 | Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering | SPCC | TNNLS | - | |
| 2024 | Deep Incomplete Multiview Clustering via Local and Global Pseudo-Label Propagation | PLP-IMVC | TNNLS | - | |
| 2024 | Robust Multi-Graph Contrastive Network for Incomplete Multi-View Clustering | RMGC | TMM | - | |
| 2024 | A novel Federated Multi-view Clustering Method for Unaligned and Incomplete Data Fusion | FUCIF | IF | ||
| 2024 | Contrastive and Adversarial Regularized Multi-level Representation Learning for Incomplete Multi-view Clustering | MRL_CAL | NN | ||
| 2024 | View-interactive Attention Information Alignment-guided Fusion for Incomplete Multi-view Clustering | VAIAF | ESA | - | |
| 2024 | Graph-Guided Imputation-Free Incomplete Multi-View Clustering | GIMVC | ESA | ||
| 2024 | Deep Incomplete Multi-View Clustering via Attention-Based Direct Contrastive Learning | ADCL | ESA | - | |
| 2024 | Incomplete Multi-View Clustering via Diffusion Completion | IMVCDC | MTA | - | |
| 2024 | Incomplete Multi-View Clustering Via Inference and Evaluation | IMVC-IE | ICASSP | ||
| 2024 | Incomplete Multi-view Clustering via Self-attention Networks and Feature Reconstruction | SNFR | APPL INTELL | - | |
| 2023 | UNTIE: Clustering Analysis with Disentanglement in Multi-view Information Fusion | UNTIE | IF | - | |
| 2023 | Federated Deep Multi-View Clustering with Global Self-Supervision | FedDMVC | ACM MM | - | |
| 2023 | Incomplete Multi-view Clustering via Attention-based Contrast Learning | MCAC | IJMLC | ||
| 2023 | Incomplete Multi-View Clustering With Complete View Guidance | IMC-CVG | SPL | - | |
| 2023 | Information Recovery-driven Deep Incomplete Multiview Clustering Network | RecFormer | TNNLS | ||
| 2023 | Realize Generative Yet Complete Latent Representation for Incomplete Multi-View Learning | CMVAE | TPAMI | - | |
| 2023 | Semantic Invariant Multi-View Clustering With Fully Incomplete Information | SMILE | TPAMI | ||
| 2023 | Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment | CPSPAN | CVPR | ||
| 2023 | Adaptive Feature Projection with Distribution Alignment for Deep Incomplete Multi-view Clustering | APADC | TIP | ||
| 2023 | Incomplete Multi-view Clustering via Prototype-based Imputation | ProImp | IJCAI | ||
| 2023 | Consistent Graph Embedding Network with Optimal Transport for Incomplete Multi-view Clustering | CGEN-OT | IS | - | |
| 2023 | CCR-Net: Consistent Contrastive Representation Network for Multi-view Clustering | CCR-Net | IS | ||
| 2023 | Incomplete Multi-view Clustering Network via Nonlinear Manifold Embedding and Probability-Induced Loss | IMCNet-MP | NN | - | |
| 2022 | Robust Multi-view Clustering with Incomplete Information | SURE | TPAMI | ||
| 2022 | Dual Contrastive Prediction for Incomplete Multi-view Representation Learning | DCP | TPAMI | ||
| 2022 | Deep Safe Incomplete Multi-view Clustering: Theorem and Algorithm | DSIMVC | ICML | ||
| 2022 | Deep Incomplete Multi-view Clustering via Mining Cluster Complementarity | DIMVC | AAAI | ||
| 2022 | Robust Diversified Graph Contrastive Network for Incomplete Multi-view Clustering | RDGC | ACM MM | ||
| 2022 | Incomplete Multi-view Clustering via Cross-view Relation Transfer | CRTC | TCSVT | - | |
| 2022 | Graph Contrastive Partial Multi-view Clustering | AGCL | TMM | ||
| 2021 | COMPLETER: Incomplete Multi-view Clustering via Contrastive Prediction | COMPLETER | CVPR | ||
| 2021 | iCmSC: Incomplete Cross-modal Subspace Clustering | iCmSC | TIP | ||
| 2021 | Generative Partial Multi-View Clustering With Adaptive Fusion and Cycle Consistency | GP-MVC | TIP | ||
| 2021 | Clustering-Induced Adaptive Structure Enhancing Network for Incomplete Multi-View Data | CASEN | IJCAI | - | |
| 2021 | Structural Deep Incomplete Multi-view Clustering Network | SDIMC-net | CIKM | - | |
| 2021 | Dual Alignment Self-Supervised Incomplete Multi-View Subspace Clustering Network | DASIMSC | SPL | - | |
| 2020 | Deep Partial Multi-View Learning | DPML | TPAMI | ||
| 2020 | CDIMC-net：Cognitive Deep Incomplete Multi-view Clustering Network( | CDIMC-net | IJCAI | ||
| 2020 | DIMC-net：Deep Incomplete Multi-view Clustering Network | DIMC-net | ACM MM | - | |
| 2020 | Deep Incomplete Multi-View Multiple Clusterings | DiMVMC | ICDM | ||
| 2019 | CPM-Nets: Cross Partial Multi-View Networks | CPM-Nets | NeurIPS | ||
| 2019 | Adversarial Incomplete Multi-view Clustering | AIMC | IJCAI | - | |
| 2018 | Partial Multi-View Clustering via Consistent GAN | PVC-GAN | ICDM |
@inproceedings{jin2023deep,
title={Deep Incomplete Multi-view Clustering with Cross-view Partial Sample and Prototype Alignment},
author={Jin, Jiaqi and Wang, Siwei and Dong, Zhibin and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={11600--11609},
year={2023}
}
@inproceedings{wangevaluate,
title={Evaluate then Cooperate: Shapley-based View Cooperation Enhancement for Multi-view Clustering},
author={Wang, Fangdi and Jin, Jiaqi and Hu, Jingtao and Liu, Suyuan and Yang, Xihong and Wang, Siwei and Liu, Xinwang and Zhu, En},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems}
}
@article{wang2022align,
title={Align then fusion: Generalized large-scale multi-view clustering with anchor matching correspondences},
author={Wang, Siwei and Liu, Xinwang and Liu, Suyuan and Jin, Jiaqi and Tu, Wenxuan and Zhu, Xinzhong and Zhu, En},
journal={Advances in Neural Information Processing Systems},
volume={35},
pages={5882--5895},
year={2022}
}
@inproceedings{dong2023cross,
title={Cross-view topology based consistent and complementary information for deep multi-view clustering},
author={Dong, Zhibin and Wang, Siwei and Jin, Jiaqi and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={19440--19451},
year={2023}
}
@inproceedings{yang2023dealmvc,
title={Dealmvc: Dual contrastive calibration for multi-view clustering},
author={Yang, Xihong and Jiaqi, Jin and Wang, Siwei and Liang, Ke and Liu, Yue and Wen, Yi and Liu, Suyuan and Zhou, Sihang and Liu, Xinwang and Zhu, En},
booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
pages={337--346},
year={2023}
}
@inproceedings{wang2024view,
title={View Gap Matters: Cross-view Topology and Information Decoupling for Multi-view Clustering},
author={Wang, Fangdi and Jin, Jiaqi and Dong, Zhibin and Yang, Xihong and Feng, Yu and Liu, Xinwang and Zhu, Xinzhong and Wang, Siwei and Liu, Tianrui and Zhu, En},
booktitle={Proceedings of the 32nd ACM International Conference on Multimedia},
pages={8431--8440},
year={2024}
}
@article{dong2024subgraph,
title={Subgraph Propagation and Contrastive Calibration for Incomplete Multiview Data Clustering},
author={Dong, Zhibin and Jin, Jiaqi and Xiao, Yuyang and Xiao, Bin and Wang, Siwei and Liu, Xinwang and Zhu, En},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2024},
publisher={IEEE}
}
@article{dong2023iterative,
title={Iterative deep structural graph contrast clustering for multiview raw data},
author={Dong, Zhibin and Jin, Jiaqi and Xiao, Yuyang and Wang, Siwei and Zhu, Xinzhong and Liu, Xinwang and Zhu, En},
journal={IEEE Transactions on Neural Networks and Learning Systems},
year={2023},
publisher={IEEE}
}