Collections for Awesome-Semi-supervised-Multi-view-classification methods (papers and codes). We are looking forward for other participants to share their papers and codes. If interested, please contact wanxinhang@nudt.edu.cn.
Update at May 2024.
- Graph SSMVC
- Depresentation Learning SSMVC.
- NMF based SSMVC.
- Other Methods.
- Multi view clustering links
| Paper | Year | Publish | Code | |
|---|---|---|---|---|
| Generative Essential Graph Convolutional Network for Multi-view Semi-supervised Classification (GEGCN) | 2024 | TMM | link | python |
| Label-Weighted Graph-Based Learning for Semi-Supervised Classification Under Label Noise (LWGL) | 2024 | TBD | link | - |
| Learnable Graph Convolutional Network and Feature Fusion for Multi-view Learning (LGCN-FF) | 2023 | Inform Fusion | link | python |
| Joint learning of feature and topology for multi-view graph convolutional network (JFGCN) | 2023 | NN | link | python |
| Interpretable Graph Convolutional Network for Multi-View Semi-Supervised Learning (IMvGCN) | 2023 | TMM | link | python |
| Fast Multi-View Semi-Supervised Learning With Learned Graph (FMSSL) | 2022 | TKDE | link | matlab |
| Semi-Supervised and Self-Supervised Classification with Multi-View Graph Neural Networks (MV-CGC) | 2021 | CIKM | link | - |
| Co-GCN for Multi-View Semi-Supervised Learning (Co-GCN) | 2020 | AAAI | link | python |
| Latent Multi-view Semi-Supervised Classification (LMSSC) | 2019 | ACML | link | matlab |
| Multi-View Clustering and Semi-Supervised Classification with Adaptive Neighbours (MLAN) | 2017 | AAAI | link | matlab |
| Scalable Multi-View Semi-Supervised Classification via Adaptive Regression (MVAR) | 2017 | TIP | link | matlab |
| Parameter-Free Auto-Weighted Multiple Graph Learning: A Framework for Multiview Clustering and Semi-Supervised Classification (AMGL) | 2016 | IJCAI | link | matlab |
| Paper | Year | Publish | Code | |
|---|---|---|---|---|
| Learning Deep Sparse Regularizers With Applications to Multi-View Clustering and Semi-Supervised Classification (DSRL) | 2022 | TPAMI | link | python |
| A semi-supervised label-driven auto-weighted strategy for multi-view data classification (LACK) | 2022 | KBS | link | matlab |
| MMatch: Semi-Supervised Discriminative Representation Learning for Multi-View Classification (MMatch) | 2022 | TCSVT | link | - |
| Semi-Supervised Multi-View Deep Discriminant Representation Learning (SMDDRL) | 2021 | TPAMI | link | - |
| Embedding Regularizer Learning for Multi-View Semi-Supervised Classification (ERL-MVSC) | 2021 | TIP | link | matlab |
| Paper | Year | Publish | Code | |
|---|---|---|---|---|
| Label prediction based constrained non-negative matrix factorization for semi-supervised multi-view classification (LPCNMF) | 2022 | Neurocomputing | link | - |
| Co-consensus semi-supervised multi-view learning with orthogonal non-negative matrix factorization (CONMF) | 2022 | INFORM PROCESS MANAG | link | matlab |
| Semi-supervised multi-view learning by using label propagation based non-negative matrix factorization (LPNMF) | 2021 | KBS | link | - |
| Semi-supervised multi-view clustering with Graph-regularized Partially Shared Non-negative Matrix Factorization (GPSNMF) | 2020 | KBS | link | matlab |
| Paper | Year | Publish | Code | |
|---|---|---|---|---|
| Seeded random walk for multi-view semi-supervised classification (SRW-MSC) | 2022 | KBS | link | - |
| Generative View-Correlation Adaptation for Semi-supervised Multi-view Learning (GVCA) | 2020 | ECCV | link | python |
| Multiview Classification With Cohesion and Diversity (MCCD) | 2020 | TCYB | link | matlab |
| Joint consensus and diversity for multi-view semi-supervised classification (JCD) | 2020 | Machine Learning | link | matlab |
https://github.com/wanxinhang/Awesome-Continual-Multi-view-clustering
https://github.com/wangsiwei2010/awesome-multi-view-clustering
https://github.com/liangnaiyao/multiview_learning
https://github.com/Jeaninezpp/Incomplete-multi-view-clustering#incomplete-multi-view-clustering.
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- We are looking forward to any comments or discussions or contributions on this topic. :)