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- W2898585087 abstract "Abstract 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 ℓ 1 minimization programs while considerably improving the computational efficiency for small values of the sparsity level." @default.
- W2898585087 created "2018-11-02" @default.
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- W2898585087 date "2020-01-01" @default.
- W2898585087 modified "2023-09-23" @default.
- W2898585087 title "Sparse Approximation of Multivariate Functions from Small Datasets Via Weighted Orthogonal Matching Pursuit" @default.
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- W2898585087 doi "https://doi.org/10.1007/978-3-030-39647-3_49" @default.
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