Feb. 12, 2024, 5:43 a.m. | Lexing Ying

cs.LG updates on arXiv.org arxiv.org

This paper 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 paper 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 …

approximation challenges construction cs.it cs.lg cs.na data data-driven eess.sp examples form fourier function general locations math.it math.na nature noise paper recovery sample unstructured values

Research Scholar (Technical Research)

@ Centre for the Governance of AI | Hybrid; Oxford, UK

HPC Engineer (x/f/m) - DACH

@ Meshcapade GmbH | Remote, Germany

Business Consultant-AI/ML

@ Bosch Group | Bengaluru, India

Senior Network Defense Analyst (AI/ML) - Hybrid

@ Noblis | Linthicum, MD, United States

Senior Data Analyst

@ Peloton | New York City

SC2024-003425 Data Scientist (NS) - WED 6 Mar

@ EMW, Inc. | Brussels, Brussels, Belgium