L-1 regularization for reconstruction of a non-equilibrium Ising model
2014 (English)In: Physica Scripta, ISSN 0031-8949, E-ISSN 1402-4896, Vol. 89, no 10, 105002- p.Article in journal (Refereed) Published
The couplings in a sparse asymmetric, asynchronous Ising network are reconstructed using an exact learning algorithm. L-1 regularization is used to remove the spurious weak connections that would otherwise be found by simply maximizing the log likelihood of a finite data set. In order to see how L-1 regularization works in detail, we perform the calculation in several ways including (1) by iterative minimization of a cost function equal to minus the log likelihood of the data plus an L-1 penalty term, and (2) an approximate scheme based on a quadratic expansion of the cost function around its minimum. In these schemes, we track how connections are pruned as the strength of the L-1 penalty is increased from zero to large values. The performance of the methods for various coupling strengths is quantified using receiver operating characteristic curves, showing that increasing the coupling strength improves reconstruction quality.
Place, publisher, year, edition, pages
2014. Vol. 89, no 10, 105002- p.
sparse networks, nonequilibrium ising model, network reconstruction
IdentifiersURN: urn:nbn:se:su:diva-109823DOI: 10.1088/0031-8949/89/10/105002ISI: 000343643400002OAI: oai:DiVA.org:su-109823DiVA: diva2:769117