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ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK
Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking, matrix completion, among others. While a large variety of bounds are available for average errors between empirical and...
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| Udgivet i: | Ann Stat |
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| Main Authors: | , , , |
| Format: | Artigo |
| Sprog: | Inglês |
| Udgivet: |
2020
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| Fag: | |
| Online adgang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC8046180/ https://ncbi.nlm.nih.gov/pubmed/33859446 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1214/19-aos1854 |
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