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Eigenmatrix for unstructured sparse recovery
November 28, 2023 · Declared Dead · 🏛 Applied and Computational Harmonic Analysis
"No code URL or promise found in abstract"
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Authors
Lexing Ying
arXiv ID
2311.16609
Category
math.NA: Numerical Analysis
Cross-listed
cs.IT,
cs.LG,
eess.SP
Citations
5
Venue
Applied and Computational Harmonic Analysis
Last Checked
2 months ago
Abstract
This note considers the unstructured sparse recovery problems in a general form. Examples include rational approximation, spectral function estimation, Fourier inversion, Laplace inversion, and sparse deconvolution. The main challenges are the noise in the sample values and the unstructured nature of the sample locations. This note proposes the eigenmatrix, a data-driven construction with desired approximate eigenvalues and eigenvectors. The eigenmatrix offers a new way for these sparse recovery problems. Numerical results are provided to demonstrate the efficiency of the proposed method.
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