Best bases for signal spaces
نویسندگان
چکیده
Article history: Received 2 October 2016 Accepted 3 October 2016 Available online xxxx Presented by Haïm Brézis We discuss the topic of selecting optimal orthonormal bases for representing classes of signals defined either through statistics or via some deterministic characterizations, or combinations of the two. In all cases, the best bases result from spectral analysis of a Hermitian matrix that summarizes the prior information we have on the signals we want to represent, achieving optimal progressive approximations. We also provide uniqueness proofs for the discrete cases. © 2016 Académie des sciences. Published by Elsevier Masson SAS. All rights reserved.
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تاریخ انتشار 2016