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 947201774
Phone: 5106435105

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

Dissimilaritybased 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 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.
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StructuredSparse Subspace Classification Code
 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.
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