Implementation for:
Hyper-Laplacian Regularized Multi-View Clustering with Exclusive L21 Regularization and Tensor Log-Determinant Minimization Approach
algsincludes only the proposed model.datacontains datasets: UCI digits, BBCSport, Yale, Extended YaleB, ORL. More datasets are avaiable at: Datautilsincludes some utilities scripts.
demo_MSC: demo for Multiview Subspace Clustering.
Competitors in Multi-view Subspace Clustering
| Method | Title | Year | Code | |
|---|---|---|---|---|
| NSHLRR | Laplacian regularized low-rank representation and its applications | 2015 | TPAMI | MATLAB |
| DiMSC | Diversity-induced multi-view subspace clustering | 2015 | CVPR | MATLAB |
| LMSC | Latent multi-view subspace clustering | 2017 | CVPR | MATLAB |
| HLR-M2VS | Hyper-Laplacian regularized multilinear multiview self-representations for clustering and semisupervised learning | 2018 | TCYB | MATLAB |
| FMR | Flexible Multi-View Representation Learning for Subspace Clustering | 2019 | IJCAI | MATLAB |
| RMSL | Reciprocal multi-layer subspace learning for multi-view clustering | 2019 | ICCV | MATLAB |
| SM2SC | Split multiplicative multi-view subspace clustering | 2019 | TIP | MATLAB |
| CDMGC | Measuring diversity in graph learning: a unified framework for structured multi-view clustering | 2021 | TKDE | MATLAB |
| CoMSC | Multiview subspace clustering via co-training robust data representation | 2021 | TNNLS | MATLAB |
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