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تسجيل مستخدم جديدSparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit
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Simone Brugiapaglia
تاريخ النشر
2018
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الهندسة المعلوماتية
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English
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We show the potential of greedy recovery strategies for the sparse approximation of multivariate functions from a small dataset of pointwise evaluations by considering an extension of the orthogonal matching pursuit to the setting of weighted sparsity. The proposed recovery strategy is based on a formal derivation of the greedy index selection rule. Numerical experiments show that the proposed weighted orthogonal matching pursuit algorithm is able to reach accuracy levels similar to those of weighted $ell^1$ minimization programs while considerably improving the computational efficiency for small values of the sparsity level.
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