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A Splicing Approach to Best Subset of Groups Selection

Authors :
Yanhang Zhang
Junxian Zhu
Jin Zhu
Xueqin Wang
Publication Year :
2021
Publisher :
arXiv, 2021.

Abstract

Best subset of groups selection (BSGS) is the process of selecting a small part of non-overlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper,we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under mild conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world datasets.<br />Comment: 49 pages, 7 figures

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....4263eee08538b272845846c08dc93e8a
Full Text :
https://doi.org/10.48550/arxiv.2104.12576