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Inverse Ising Inference Using All the Data
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
KTH. (Engineering Physics Program)
2012 (English)In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 108, no 9, 090201- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. Vol. 108, no 9, 090201- p.
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-92054DOI: 10.1103/PhysRevLett.108.090201ISI: 000300943400001Scopus ID: 2-s2.0-84857876275OAI: oai:DiVA.org:kth-92054DiVA: diva2:512152
Note
QC 20120326Available from: 2012-03-26 Created: 2012-03-26 Last updated: 2017-12-07Bibliographically approved

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