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

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-05-09 DOI:10.1080/10618600.2024.2353640

Nan Qiao, Canyi Chen

引用次数: 0

Abstract

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 ...

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分散低阶学习是一个活跃的研究领域，有着广泛的实际应用。产生低秩和稳健估计的一种常见方法是结合...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.