Submatrix localization via message passing

Bruce Hajek, Yihong Wu, Jiaming Xu

Research output: Contribution to journalArticle

Abstract

The principal submatrix localization problem deals with recovering a K ×K principal submatrix of elevated mean µ in a large n × n symmetric matrix subject to additive standard Gaussian noise, or more generally, mean zero, variance one, subgaussian noise. This problem serves as a prototypical example for community detection, in which the community corresponds to the support of the submatrix. The main result of this paper is that in the regime Ω(n) ≤ K ≤ o(n), the support of the submatrix can be weakly recovered (with o(K) misclassification errors on average) by an optimized message passing algorithm if λ = µ2K2/n, the signal-to-noise ratio, exceeds 1/e. This extends a result by Deshpande and Montanari previously obtained for K = Θ(n) and µ = Θ(1). In addition, the algorithm can be combined with a voting procedure to achieve the information-theoretic limit of exact recovery with sharp constants for all K ≥log n n(8 1 e + o(1)). The total running time of the algorithm is O(n2 log n). Another version of the submatrix localization problem, known as noisy biclustering, aims to recover a K1 × K2 submatrix of elevated mean µ in a large n1 × n2 Gaussian matrix. The optimized message passing algorithm and its analysis are adapted to the bicluster problem assuming Ω(ni) ≤ Ki ≤ o(ni) and K1 K2. A sharp information-theoretic condition for the weak recovery of both clusters is also identified.

Original languageEnglish (US)
Pages (from-to)1-52
Number of pages52
JournalJournal of Machine Learning Research
Volume18
StatePublished - Apr 1 2018

Keywords

  • Biclustering
  • High-dimensional statistics
  • Message passing
  • Spectral algorithms computational complexity
  • Submatrix localization

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

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