Robust Parallel Pursuit for Large-Scale Association Network Learning

Wenhui Li (), Xin Zhou (), Ruipeng Dong () and Zemin Zheng ()
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Wenhui Li: International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
Xin Zhou: International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
Ruipeng Dong: International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China
Zemin Zheng: International Institute of Finance, The School of Management, University of Science and Technology of China, Hefei, Anhui 230026, China

INFORMS Journal on Computing, 2025, vol. 37, issue 2, 428-445

Abstract: Sparse reduced-rank regression is an important tool to uncover the large-scale response-predictor association network, as exemplified by modern applications such as the diffusion networks, and recommendation systems. However, the association networks recovered by existing methods are either sensitive to outliers or not scalable under the big data setup. In this paper, we propose a new statistical learning method called robust parallel pursuit (ROP) for joint estimation and outlier detection in large-scale response-predictor association network analysis. The proposed method is scalable in that it transforms the original large-scale network learning problem into a set of sparse unit-rank estimations via factor analysis, thus facilitating an effective parallel pursuit algorithm. Furthermore, we provide comprehensive theoretical guarantees including consistency in parameter estimation, rank selection, and outlier detection, and we conduct an inference procedure to quantify the uncertainty of existence of outliers. Extensive simulation studies and two real-data analyses demonstrate the effectiveness and the scalability of the suggested approach.

Keywords: large-scale association network; outlier detection; robust estimation; sparse reduced-rank regression; scalability; parallel pursuit (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:2:p:428-445

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