Cai, T. Tony; Liang, Tengyuan; Rakhlin, Alexander Computational and statistical boundaries for submatrix localization in a large noisy matrix. (English) Zbl 1392.62017 Ann. Stat. 45, No. 4, 1403-1430 (2017). The paper deals with the analysis of the interplay between computational efficiency and statistical accuracy in submatrix localization based on a noisy observation of a large matrix. In this respect the authors propose and analyse a new linear time spectral algorithm that identifies the submatrix. They show that the localization problem requires larger signal-to-noise ratio. They also consider a new analysis of a known method that is based on a convex relaxation for comparison with the spectral method. Reviewer: Doina Carp (Constanta) Cited in 18 Documents MSC: 62C20 Minimax procedures in statistical decision theory 90C27 Combinatorial optimization Keywords:computational boundary; computational complexity; detection; planted clique; lower bounds; minimax; signal-to-noise ratio; statistical boundary; submatrix localization × Cite Format Result Cite Review PDF Full Text: DOI arXiv Euclid