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Sparce Subspace Clustering (SSC) is a subspace clustering algorithm that uses sparse vector representation, convex optimization, and spectral clustering.
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JHUVisionLab/SSC-using-ADMM
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{\rtf1\ansi\ansicpg1252\cocoartf1138\cocoasubrtf510 {\fonttbl\f0\fswiss\fcharset0 Helvetica;} {\colortbl;\red255\green255\blue255;} \margl1440\margr1440\vieww17620\viewh12960\viewkind0 \deftab720 \pard\pardeftab720 \f0\fs26 \cf0 ---------------------------------------------------------------------------------------------------------------------\ Copyright @ Ehsan Elhamifar, 2012\ \ ---------------------------------------------------------------------------------------------------------------------\ To run the Sparse Subspace Clustering (SSC) algorithm\ \ for motion segmentation on the Hopkins 155 dataset, see the following m-file: run_SSC_MS.m. \ for face clustering on the Extended Yale B dataset, see the following m-file: run_SSC_Faces.m. \ \ ---------------------------------------------------------------------------------------------------------------------\ Terms of use: \ The code is provided for research purposes only and without any warranty. Any commercial use is prohibited. \ \ When using the code in your research work, you should cite the following paper:\ \ Sparse Subspace Clustering: Algorithm, Theory, and Applications\ E. Elhamifar and R. Vidal, \ Submitted to IEEE Trans. on PAMI, 2011.\ Available: {\field{\*\fldinst{HYPERLINK "http://arxiv.org/abs/1203.1005"}}{\fldrslt http://arxiv.org/abs/1203.1005}}\ \ ---------------------------------------------------------------------------------------------------------------------\ Please contact Ehsan Elhamifar (ehsan [At] cis [Dot] jhu [Dot] edu) for questions about the code.}
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Sparce Subspace Clustering (SSC) is a subspace clustering algorithm that uses sparse vector representation, convex optimization, and spectral clustering.
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