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Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
Fast Sparse Representation with Prototypes (CVPR 2010)
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Fast Sparse Representation with Prototypes (CVPR 2010)

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Sparse representation has found applications in numerous domains and recent developments have been focused on the convex relaxation of the `0-norm minimization for sparse coding (i.e., the `1-norm …

Sparse representation has found applications in numerous domains and recent developments have been focused on the convex relaxation of the `0-norm minimization for sparse coding (i.e., the `1-norm minimization). Nevertheless, the time and space complexities of these algorithms remain significantly high for large-scale problems. As signals in most problems can be modeled by a small set of prototypes, we propose an algorithm that exploits this property and show that the `1-norm minimization problem can be reduced to a much smaller problem, thereby gaining significant speed-ups with much less memory requirements. Experimental results demonstrate that our algorithm is able to achieve double-digit gain in speed with much less memory requirement than the state-of-the-art algorithms.

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