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We consider the problem of classification via two-dimensional underdetermined random projection and sparse representation. We contend that the two-dimensional underdetermine random projection has a natural relationship with deterministic underdetermined projections, such as 2DPCA and (2D)2PCA but is more efficient in terms of the computational complexity for feature extraction. The proposed projection...
We consider the feature extraction problem based on compressive sampling for supervised image classification. Inspired by recently emerged 1D compressive sampling (1DCS) and 2DPCA techniques, a novel 2D compressive sampling method, called 2DCS, using two random underdetermined projections, is proposed. 2DCS data could be effectively used for pattern representation. Moreover, original data could be...
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