Codes
Dissimilaritybased Sparse Subset Selection (DS3)
 Dissimilaritybased Sparse Subset Selection (DS3) is an algorithm based on simultaneous sparse recovery for finding data/model representatives from a large collection of data/models.
 We provide a MATLAB implementation of DS3. When using the code in your research work, you should cite the following paper:
E. Elhamifar, G. Sapiro and S. Sastry, Dissimilaritybased Sparse Subset Selection, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.
Download Code
Sparse Subspace Clustering (SSC)
 Sparse Subspace Clustering (SSC) is an algorithm based on sparse representation theory for segmentation of data lying in a union of subspaces. For more information please visit the SSC research page.
 We provide a MATLAB implementation of SSC. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Sparse Subspace Clustering: Algorithm, Theory, and Applications, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2013.
Download Code (ADMM)
Download Code (CVX)
Sparse Manifold Clustering and Embedding (SMCE)
 Sparse Manifold Clustering and Embedding (SMCE) is an algorithm based on sparse representation theory for clustering and dimensionality reduction of data lying in a union of nonlinear manifolds.
 We provide a MATLAB implementation of SMCE algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Sparse Manifold Clustering and Embedding, Neural Information Processing Systems (NIPS), 2011.
Download Code
Sparse Modeling Representative Selection (SMRS)
 Sparse Modeling Representative Selection (SMRS) is an algorithm based on sparse multiplemeasurementvector recovery theory for selecting a subset of data points as the representatives.
 We provide a MATLAB implementation of SMRS algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar, G. Sapiro, and R. Vidal, See All by Looking at A Few: Sparse Modeling for Finding Representative Objects, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
Download Code
BlockSparse Subspace Classification (BSSC)
 StructuredSparse Subspace Classification is an algorithm based on blocksparse representation techniques for classifying multisubspace data, where the training data in each class lie in a union of subspaces.
 We provide a MATLAB implementation of the algorithm. When using the code in your research work, you should cite the following paper:
E. Elhamifar and R. Vidal, Robust Classification using Structured Sparse Representation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2011.
Download Code
