Ehsan Elhamifar

Dissimilarity-based Sparse Subset Selection Code

• Dissimilarity-based Sparse Subset Selection (DS3) is an algorithm based on simultaneous sparse recovery for finding data/model representatives.


• We provide a MATLAB implementation of DS3. When using the code in your research work, you should cite the following papers:
  
  E. Elhamifar, G. Sapiro and S. Sastry, Dissimilarity-based Sparse Subset Selection
  IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014 (under review).
  
  E. Elhamifar, G. Sapiro and R. Vidal, Finding Exemplars from Pairwise Dissimilarities via Simultaneous Sparse Recovery
  Advances in Neural Information Processing Systems (NIPS), 2012.

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Sparse Subspace Clustering Code

• 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.

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Sparse Manifold Clustering and Embedding Code

• 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
  Advances in Neural Information Processing Systems (NIPS), 2011.

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Sparse Modeling Representative Selection Code

• Sparse Modeling Representative Selection (SMRS) is an algorithm based on sparse multiple-measurement-vector 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.

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Structured-Sparse Subspace Classification Code

• Structured-Sparse Subspace Classification is an algorithm based on block-sparse representation techniques for classifying multi-subspace 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.

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