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Dissimilarity-based Sparse Subset Selection (DS3)

  • Dissimilarity-based 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,
    Dissimilarity-based Sparse Subset Selection,
    IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2016.

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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.

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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.

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Sparse Modeling Representative Selection (SMRS)

  • 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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Block-Sparse Subspace Classification (BSSC)

  • 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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