Fast and Robust Low-Rank Learning over Networks: A Decentralized Matrix Quantile Regression Approach

Decentralized low-rank learning is an active research domain with extensive practical applications. A common approach to producing low-rank and robust estimations is to employ a combination of the nonsmooth quantile regression loss and nuclear-norm regularizer. Nevertheless, directly applying existi...

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Veröffentlicht in:	Journal of computational and graphical statistics Jg. 33; H. 4; S. 1214 - 1223
Hauptverfasser:	Qiao, Nan, Chen, Canyi
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria Taylor & Francis 01.10.2024 Taylor & Francis Ltd
Schlagworte:	Algorithms Convergence Decentralized network Efficiency Heavy-tailed noise Learning Low-rank recovery Quantile regression Quantiles Regression Robustness Scalable algorithms Statistical analysis
ISSN:	1061-8600, 1537-2715
Online-Zugang:	Volltext
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