Low-rank matrix recovery under heavy-tailed errors-Reference-Cited by-同舟云学术

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Low-rank matrix recovery under heavy-tailed errors

Published:2024-08-01 Issue:3 Volume:30 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Yu Myeonghun¹,Sun Qiang²,Zhou Wen-Xin³

Affiliation:

1. Department of Mathematics, University of California, San Diego, La Jolla, CA, 92093, USA

2. Department of Statistical Sciences, University of Toronto, Toronto, ON M5G 1Z5, Canada

3. Department of Information and Decision Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Reference43 articles.

1. Fan, J., Li, Q. and Wang, Y. (2017). Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. J. R. Stat. Soc. Ser. B. Stat. Methodol. 79 247–265. 10.1111/rssb.12166

2. Fan, J., Liu, H., Sun, Q. and Zhang, T. (2018). I-LAMM for sparse learning: Simultaneous control of algorithmic complexity and statistical error. Ann. Statist. 46 814–841. 10.1214/17-AOS1568

3. Fan, J., Wang, W. and Zhu, Z. (2021). A shrinkage principle for heavy-tailed data: High-dimensional robust low-rank matrix recovery. Ann. Statist. 49 1239–1266. 10.1214/20-aos1980

4. Candès, E.J. and Recht, B. (2009). Exact matrix completion via convex optimization. Found. Comput. Math. 9 717–772. 10.1007/s10208-009-9045-5

5. Candès, E.J. and Plan, Y. (2011). Tight oracle inequalities for low-rank matrix recovery from a minimal number of noisy random measurements. IEEE Trans. Inf. Theory 57 2342–2359. 10.1109/TIT.2011.2111771

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