Recovering Structured Data From Superimposed Non-Linear Measurements

DOI10.1109/TIT.2019.2932426zbMath1433.94023arXiv1708.07451OpenAlexW3102477378WikidataQ114628644 ScholiaQ114628644MaRDI QIDQ5211610

Martin Genzel, Peter Jung

Publication date: 28 January 2020

Published in: IEEE Transactions on Information Theory (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1708.07451

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Nonparametric estimation (62G05) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (5)

Generic error bounds for the generalized Lasso with sub-exponential data ⋮ A unified approach to uniform signal recovery from nonlinear observations ⋮ Distributed Decoding From Heterogeneous 1-Bit Compressive Measurements ⋮ Applied harmonic analysis and data processing. Abstracts from the workshop held March 25--31, 2018 ⋮ Optimal combination of linear and spectral estimators for generalized linear models

This page was built for publication: Recovering Structured Data From Superimposed Non-Linear Measurements