Title :
Sparse recovery and Kronecker products
Author_Institution :
Dept. of Math., Tech. Univ. of Berlin, Berlin, Germany
Abstract :
In this note will consider sufficient conditions for sparse recovery such as Spark, coherence, restricted isometry property (RIP) and null space property (NSP). Then we will discuss the solution of underdetermined linear equations when the matrix is the Kronecker product of matrices. Specially we will explain how NSP behave in the case where the matrix is the Kronecker product of matrices.
Keywords :
matrix multiplication; signal processing; sparse matrices; Kronecker products; matrices; null space property; restricted isometry property; sparse signal recovery; Compressed sensing; Equations; Linear systems; Minimization methods; Null space; Pursuit algorithms; Sparks; Sparse matrices; Sufficient conditions; Vectors; Kronecker product; Spark; coherence; compressed sensing; null space property; restricted isometry property; sparse solution of linear systems;
Conference_Titel :
Information Sciences and Systems (CISS), 2010 44th Annual Conference on
Conference_Location :
Princeton, NJ
Print_ISBN :
978-1-4244-7416-5
Electronic_ISBN :
978-1-4244-7417-2
DOI :
10.1109/CISS.2010.5464722
