Inverse finite-size scaling for high-dimensional significance analysis

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dc.contributor University of Helsinki, Department of Computer Science en
dc.contributor University of Helsinki, Jukka Corander / Principal Investigator en
dc.contributor.author Xu, Yingying
dc.contributor.author Puranen, Santeri
dc.contributor.author Corander, Jukka
dc.contributor.author Kabashima, Yoshiyuki
dc.date.accessioned 2019-11-20T12:35:01Z
dc.date.available 2019-11-20T12:35:01Z
dc.date.issued 2018-06-06
dc.identifier.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 . https://doi.org/10.1103/PhysRevE.97.062112 en
dc.identifier.issn 2470-0045
dc.identifier.other PURE: 107996458
dc.identifier.other PURE UUID: 1b51a719-c934-471b-839a-bcf8b79aba60
dc.identifier.other WOS: 000434259700003
dc.identifier.other Scopus: 85048212207
dc.identifier.uri http://hdl.handle.net/10138/307116
dc.description.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. en
dc.format.extent 9
dc.language.iso eng
dc.relation.ispartof Physical Review E
dc.rights en
dc.subject APPROXIMATE BAYESIAN COMPUTATION en
dc.subject DIRECT-COUPLING ANALYSIS en
dc.subject PROTEIN-STRUCTURE en
dc.subject INFERENCE en
dc.subject CONTACTS en
dc.subject MODEL en
dc.subject 111 Mathematics en
dc.subject 113 Computer and information sciences en
dc.title Inverse finite-size scaling for high-dimensional significance analysis en
dc.type Article
dc.description.version Peer reviewed
dc.identifier.doi https://doi.org/10.1103/PhysRevE.97.062112
dc.type.uri info:eu-repo/semantics/other
dc.type.uri info:eu-repo/semantics/publishedVersion
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