This session discusses several extensions of the basic sparse representation concept, from the original l-1 minimization formulation to group sparsity, Sparse PCA, Robust PCA, and compressive phase retrieval. These variations extend the applications of compressive sensing to multiple-view objection recognition, informative feature selection, and medical imaging. Efficient numerical algorithms are a focus of our discussion, which are responsible for recovering stable estimates of the sparse signals in high-dimensional space. Finally, we briefly discuss how to properly implement the sparsity minimization algorithms on modern many-core CPU/GPU environments. Joint talk by Yi Ma and John Wright.
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