Inverse finite-size scaling for high-dimensional significance analysis

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018-06-06
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Language
en
Pages
1-9
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Physical Review E, Volume 97, issue 6
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.
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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