Robust one-bit compressed sensing with partial circulant matrices
Author:
Affiliation:
1. Mathematical Institute, Utrecht University
2. Mathematical Sciences Institute, The Australian National University
Publisher
Institute of Mathematical Statistics
Subject
Statistics, Probability and Uncertainty,Statistics and Probability
Reference22 articles.
1. BARANIUK, R. G., FOUCART, S., NEEDELL, D., PLAN, Y. and WOOTTERS, M. (2017). Exponential decay of reconstruction error from binary measurements of sparse signals. IEEE Trans. Inf. Theory 63 3368–3385.
2. DIRKSEN, S. and MENDELSON, S. (2021). Non-Gaussian hyperplane tessellations and robust one-bit compressed sensing. J. Eur. Math. Soc. (JEMS) 23 2913–2947.
3. PLAN, Y. and VERSHYNIN, R. (2013). Robust 1-bit compressed sensing and sparse logistic regression: A convex programming approach. IEEE Trans. Inf. Theory 59 482–494.
4. PLAN, Y. and VERSHYNIN, R. (2013). One-bit compressed sensing by linear programming. Comm. Pure Appl. Math. 66 1275–1297.
5. MENDELSON, S. (2017). “Local” vs. “global” parameters—breaking the Gaussian complexity barrier. Ann. Statist. 45 1835–1862.
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