name of picture

Ehsan Elhamifar

EECS Department
University of California, Berkeley

Email:
ehsan [at] eecs [dot] berkeley [dot] edu

Address:
University of California, Berkeley
TRUST Center
Room 337 Cory Hall
Engineering Department
Berkeley, Ca 94720-1774

Phone:
510-643-5105

Ehsan Elhamifar

Postdoctoral Fellow
Electrical Engineering and Computer Science Department
University of California, Berkeley

Dissimilarity-based Sparse Subset Selection Code


Sparse Subspace Clustering Code


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.

  • Download Code

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