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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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Bibliographische Detailangaben
Veröffentlicht in:Ann Stat
Hauptverfasser: Abbe, Emmanuel, Fan, Jianqing, Wang, Kaizheng, Zhong, Yiqiao
Format: Artigo
Sprache:Inglês
Veröffentlicht: 2020
Schlagworte:
Online Zugang: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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