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This paper investigates a new learning formulation called dynamic group sparsity. It is a natural extension of the standard sparsity concept in compressive sensing, and is motivated by the observation that in some practical sparse data the nonzero coefficients are often not random but tend to be clustered. Intuitively, better results can be achieved in these cases by reasonably utilizing both clustering...
Sparse representation in compressive sensing is gaining increasing attention due to its success in various applications. As we demonstrate in this paper, however, image sparse representation is sensitive to image plane transformations such that existing approaches can not reconstruct the sparse representation of a geometrically transformed image. We introduce a simple technique for obtaining transformation-invariant...
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