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.
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Sathi, L.M., Juluri, V., Tangudu, S., Sreeram, S., Kuzhithara Sajan, K., Palakkattillam, S. (2022). Simultaneous Sparse Representations with Partially Varying Support. In: Majhi, S., Prado, R.P.d., Dasanapura Nanjundaiah, C. (eds) Distributed Computing and Optimization Techniques. Lecture Notes in Electrical Engineering, vol 903. Springer, Singapore. https://doi.org/10.1007/978-981-19-2281-7_72
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DOI: https://doi.org/10.1007/978-981-19-2281-7_72
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