INTERDISCIPLINARY MATHEMATICS INSTITUTE 2010 : 01 Orthogonal super greedy algorithm and application in compressed sensing IMI
نویسنده
چکیده
The general theory of greedy approximation is well developed. Much less is known about how specific features of a dictionary can be used for our advantage. In this paper we discuss incoherent dictionaries. We build a new greedy algorithm which is called the Orthogonal Super Greedy Algorithm (OSGA). OSGA is more efficient than a standard Orthogonal Greedy Algorithm (OGA). We show that the rates of convergence of OSGA and OGA with respect to incoherent dictionaries are the same. Greedy approximation is also a fundamental tool for sparse signal recovery. The performance of Orthogonal Multi Matching Pursuit (OMMP), a counterpart of OSGA in the compressed sensing setting, is also analyzed under RIP conditions.
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