Inverse finite-size scaling for high-dimensional significance analysis
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
Series
Physical Review E, Volume 97, issue 6, pp. 1-9
Abstract
We propose an efficient procedure for significance determination in high-dimensional dependence learning based on surrogate data testing, termed inverse finite-size scaling (IFSS). The IFSS method is based on our discovery of a universal scaling property of random matrices which enables inference about signal behavior from much smaller scale surrogate data than the dimensionality of the original data. As a motivating example, we demonstrate the procedure for ultra-high-dimensional Potts models with order of 1010 parameters. IFSS reduces the computational effort of the data-testing procedure by several orders of magnitude, making it very efficient for practical purposes. This approach thus holds considerable potential for generalization to other types of complex models.Description
Keywords
Other note
Citation
Xu, Y, Puranen, S, Corander, J & Kabashima, Y 2018, 'Inverse finite-size scaling for high-dimensional significance analysis', Physical Review E, vol. 97, no. 6, 062112, pp. 1-9. https://doi.org/10.1103/PhysRevE.97.062112