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The aim of this article is to construct the generalized random matrices, which satisfies the restricted isometry property (as introduced by Candes and Tao). Let the data be presented as a product of a vector with not more than K nonzero coordinates by a given matrix. We show that for such data we can change the upper bound of the variable K. In particular, we prove that the random matrices whose entries are independent realizations of random variables from ϕ-subgaussian space could be used in the theory of compressive sensing for encoding vectors.
Published Online: 2015-8-13
Published in Print: 2015-9-1
© 2015 by De Gruyter