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Dynamics and performance of susceptibility propagation on synthetic data
KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
2010 (English)In: European Physical Journal B: Condensed Matter Physics, ISSN 1434-6028, E-ISSN 1434-6036, Vol. 77, no 4, 587-595 p.Article in journal (Refereed) Published
Abstract [en]

We study the performance and convergence properties of the susceptibility propagation (SusP) algorithm for solving the Inverse Ising problem. We first study how the temperature parameter (T) in a Sherrington-Kirkpatrick model generating the data influences the performance and convergence of the algorithm. We find that at the high temperature regime (T > 4), the algorithm performs well and its quality is only limited by the quality of the supplied data. In the low temperature regime (T < 4), we find that the algorithm typically does not converge, yielding diverging values for the couplings. However, we show that by stopping the algorithm at the right time before divergence becomes serious, good reconstruction can be achieved down to T a parts per thousand 2. We then show that dense connectivity, loopiness of the connectivity, and high absolute magnetization all have deteriorating effects on the performance of the algorithm. When absolute magnetization is high, we show that other methods can be work better than SusP. Finally, we show that for neural data with high absolute magnetization, SusP performs less well than TAP inversion.

Place, publisher, year, edition, pages
2010. Vol. 77, no 4, 587-595 p.
Keyword [en]
statistical physics, boltzmann machines, patterns, model
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:kth:diva-27378DOI: 10.1140/epjb/e2010-00277-0ISI: 000284113100014OAI: oai:DiVA.org:kth-27378DiVA: diva2:376792
Note
QC 20101213Available from: 2010-12-13 Created: 2010-12-13 Last updated: 2017-12-11Bibliographically approved

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