Learning block structures in U-statistic-based matrices
成果类型：
Article
署名作者：
Zhang, Weiping; Jin, Baisuo; Bai, Zhidong
署名单位：
Chinese Academy of Sciences; University of Science & Technology of China, CAS; Northeast Normal University - China
刊物名称：
BIOMETRIKA
ISSN/ISSBN：
0006-3444
DOI：
10.1093/biomet/asaa099
发表日期：
2021
页码：
933946
关键词：
摘要：
We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of the approach is examined by extensive simulations and data examples.