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The parsimonious nature of sparse representations has been successfully exploited for the development of highly accurate classifiers for various scientific applications. Despite the successes of Sparse Representation techniques, a large number of dictionary atoms as well as the high dimensionality of the data can make these classifiers computationally demanding. Furthermore, sparse classifiers are...
Dimensionality reduction via manifold learning offers an elegant representation of data whereby the high dimensional feature space is parameterized by a lower dimensional space where the data resides. Sparse representations efficiently represent test patterns by sparse linear coefficients from a dictionary of training exemplars. Sparse representations have been adopted for classification purposes,...
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