Proves all-or-nothing exact alignment threshold in Gaussian multi-graph model and partial alignment impossibility threshold in sparse ER model, via a Bayesian estimation framework over metric spaces.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
math.ST 2verdicts
UNVERDICTED 2representative citing papers
Umeyama algorithm achieves exact recovery of latent permutation π* in correlated Gaussian geometric models for σ = o(d^{-3}n^{-2/d}) and almost exact for σ = o(d^{-3}n^{-1/d}) when d = O(log n).
citing papers explorer
-
The feasibility of multi-graph alignment: a Bayesian approach
Proves all-or-nothing exact alignment threshold in Gaussian multi-graph model and partial alignment impossibility threshold in sparse ER model, via a Bayesian estimation framework over metric spaces.
-
The Umeyama algorithm for matching correlated Gaussian geometric models in the low-dimensional regime
Umeyama algorithm achieves exact recovery of latent permutation π* in correlated Gaussian geometric models for σ = o(d^{-3}n^{-2/d}) and almost exact for σ = o(d^{-3}n^{-1/d}) when d = O(log n).