Title: Simultaneous Sparse Representations with Partially Varying Support
Abstract: The idea of sparse representations approximates a signal as a linear combination of a few atoms from a redundant over complete dictionary. Orthogonal Matching Pursuit (OMP) is a greedy algorithm used for the computation of sparse representations. The idea of simultaneous sparse representations is to jointly compute the sparse representations of a group of signals with a common support for their corresponding sparse representations. The OMP algorithm was later extended to Simultaneous-OMP (SOMP) for computing simultaneous sparse representations of a group of signals. The strict constraint on the support of non-zero coefficients makes SOMP unusable in many situations. In this work, an extension of the SOMP algorithm for computing simultaneous sparse representations with a partially varying support is proposed. The experiments demonstrate that the proposed algorithm achieves superior performance over SOMP, when the support of the non-zero sparse representation coefficients is not exactly same for all the sparse representations.
Publication Year: 2022
Publication Date: 2022-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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