Optimally sparse frames

Peter G. Casazza, Andreas Heinecke, Felix Krahmer, Gitta Kutyniok

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

34 Zitate (Scopus)

Abstract

Frames have established themselves as a means to derive redundant, yet stable decompositions of a signal for analysis or transmission, while also promoting sparse expansions. However, when the signal dimension is large, the computation of the frame measurements of a signal typically requires a large number of additions and multiplications, and this makes a frame decomposition intractable in applications with limited computing budget. To address this problem, in this paper, we focus on frames in finite-dimensional Hilbert spaces and introduce sparsity for such frames as a new paradigm. In our terminology, a sparse frame is a frame whose elements have a sparse representation in an orthonormal basis, thereby enabling low-complexity frame decompositions. To introduce a precise meaning of optimality, we take the sum of the numbers of vectors needed from this orthonormal basis when expanding each frame vector as sparsity measure. We then analyze the recently introduced algorithm Spectral Tetris for construction of unit norm tight frames and prove that the tight frames generated by this algorithm are in fact optimally sparse with respect to the standard unit vector basis. Finally, we show that even the generalization of Spectral Tetris for the construction of unit norm frames associated with a given frame operator produces optimally sparse frames.

OriginalspracheEnglisch
Aufsatznummer5929561
Seiten (von - bis)7279-7287
Seitenumfang9
FachzeitschriftIEEE Transactions on Information Theory
Jahrgang57
Ausgabenummer11
DOIs
PublikationsstatusVeröffentlicht - Nov. 2011
Extern publiziertJa

Fingerprint

Untersuchen Sie die Forschungsthemen von „Optimally sparse frames“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren