Kindly note that the implementations in this repository are simply our attempt at implementing the below mentioned papers, originally published by the respective authors.
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KMeans.ipynb: Basic K-Means Clustering applied on the handwritten digits dataset. Dataset Description:https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html.
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SSC_Vidal2013_IRIS.ipynb: Perform Sparse Subspace Clustering (SSC) Algorithm to cluster objects in Iris Dataset.
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SSC_Vidal2013_Wine.ipynb: Perform Sparse Subspace Clustering (SSC) Algorithm to cluster objects in Wine Dataset.
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SSC_Vidal2013_Yale.ipynb: Perform Sparse Subspace Clustering (SSC) Algorithm to cluster Faces in Yale Dataset.
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SSCE_IRIS.ipynb: Perform Sparse Subspace Clustering with Entropy-Norm (SSCE) Algorithm to cluster objects in Iris Dataset.
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SSCE_Wine.ipynb: Perform Sparse Subspace Clustering with Entropy-Norm (SSCE) Algorithm to cluster objects in Wine Dataset.
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SSCE_Yale.ipynb: Perform Sparse Subspace Clustering with Entropy-Norm (SSCE) Algorithm to cluster Faces in Yale Dataset.
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LMVSC_Caltech101-20-QP.ipynb: Perform Large-scale Multi-View Subspace Clustering in Linear Time Algorithm (LMVSC) to cluster Caltech-101-20 Dataset.
Dataset: https://github.com/yeqinglee/mvdata -
LMVSC_Handwritten-QP.ipynb: Perform Large-scale Multi-View Subspace Clustering in Linear Time Algorithm (LMVSC) to cluster Multi-View Handwritten Dataset.
Dataset: https://github.com/yeqinglee/mvdata
Note:
Codes 2-4: Follows the papers "Sparse Subspace Clustering: Algorithm, Theory, and Applications" (IEEE Trans. on PAMI 2013) by Elhamifar and Vidal (https://arxiv.org/pdf/1203.1005.pdf) and "Sparse Subspace Clustering" (CVPR 2009) by Elhamifar and Vidal (http://cis.jhu.edu/~ehsan/Downloads/SSC-CVPR09-Ehsan.pdf)
Codes 5-7: Follows the paper "Sparse Subspace Clustering with Entropy-Norm" (ICML 2020) by Bai and Liang (https://proceedings.icml.cc/static/paper_files/icml/2020/1982-Paper.pdf)
Codes 8-9: Follows the paper "Large-scale Multi-view Subspace Clustering in Linear Time" (AAAI 2020) by Kang, Zhao, Wangtao Zhou, Zhitong Zhao, Junming Shao, Meng Han, and Zenglin Xu (https://arxiv.org/pdf/1911.09290.pdf)