ENTRYWISE EIGENVECTOR ANALYSIS OF RANDOM MATRICES WITH LOW EXPECTED RANK

• 成果类型: Article
• 署名作者: Abbe, Emmanuel; Fan, Jianqing; Wang, Kaizheng; Zhong, Yiqiao
• 署名单位: Princeton University; Princeton University; Princeton University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/19-AOS1854
• 发表日期: 2020
• 页码: 1452-1474
• 关键词: Community Detection; LARGEST EIGENVALUE; completion; relaxation; RECOVERY; bounds; rates; forms

摘要

Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking, matrix completion, among others. While a large variety of bounds are available for average errors between empirical and population statistics of eigenvectors, few results are tight for entrywise analyses, which are critical for a number of problems such as community detection. This paper investigates entrywise behaviors of eigenvectors for a large class of random matrices whose expectations are low-rank, which helps settle the conjecture in Abbe, Bandeira and Hall (2014) that the spectral algorithm achieves exact recovery in the stochastic block model without any trimming or cleaning steps. The key is a first-order approximation of eigenvectors under the l(infinity) norm: u(k) approximate to Au-k*/lambda(k)*, where {u(k)} and {u(k)*} are eigenvectors of a random matrix A and its expectation EA, respectively. The fact that the approximation is both tight and linear in A facilitates sharp comparisons between u(k) and u(k)*. In particular, it allows for comparing the signs of u(k) and u(k)* even if parallel to u(k) - u(k)*parallel to(infinity) is large. The results are further extended to perturbations of eigenspaces, yielding new l(infinity)-type bounds for synchronization (Z(2)-spiked Wigner model) and noisy matrix completion.