2011/07/31 by Erik Aurell, Magnus Ekeberg · 2 citations
Physics and Astronomy · #cond-mat.dis-nn #cond-mat.stat-mech #physics.data-an
paper · pdf · doi:10.1103/physrevlett.108.090201
5 pages, 2 figures. Accepted version
arxiv created 2012/11/12 · arxiv updated 2013/05/29
We show that a method based on logistic regression, using all the data, solves the inverse Ising problem far better than mean-field calculations relying only on sample pairwise correlation functions, while still computationally feasible for hundreds of nodes. The largest improvement in reconstruction occurs for strong interactions. Using two examples, a diluted Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that interaction topologies can be recovered from few samples with good accuracy and that the use of l1-regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.