Inverse Ising inference using all the data

Details Inverse Ising inference using all the data Inverse Ising inference using all the data

2011-07-18

arxiv.org/abs/1107.3536v2

Physics

Condensed Matter

Disordered Systems and Neural Networks

5 pages, 2 figures. Version to be submitted

Scientific paper

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 $l_1$-regularization is beneficial in this process, pushing inference abilities further into low-temperature regimes.

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