000042907 001__ 42907 000042907 005__ 20230217124342.0 000042907 017__ $$aThis version is available at the following Publisher URL: http://pre.aps.org 000042907 0247_ $$2DOI$$a10.1103/PhysRevE.69.066138 000042907 0247_ $$2WOS$$aWOS:000222502800050 000042907 0247_ $$2Handle$$a2128/2365 000042907 0247_ $$2altmetric$$aaltmetric:267476 000042907 0247_ $$2pmid$$apmid:15244698 000042907 037__ $$aPreJuSER-42907 000042907 041__ $$aeng 000042907 082__ $$a530 000042907 084__ $$2WoS$$aPhysics, Fluids & Plasmas 000042907 084__ $$2WoS$$aPhysics, Mathematical 000042907 1001_ $$0P:(DE-Juel1)VDB46297$$aKraskov, A.$$b0$$uFZJ 000042907 245__ $$aEstimating Mutual Information 000042907 260__ $$aCollege Park, Md.$$bAPS$$c2004 000042907 264_1 $$2Crossref$$3online$$bAmerican Physical Society (APS)$$c2004-06-23 000042907 264_1 $$2Crossref$$3print$$bAmerican Physical Society (APS)$$c2004-06-01 000042907 300__ $$a066138 000042907 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article 000042907 3367_ $$2DataCite$$aOutput Types/Journal article 000042907 3367_ $$00$$2EndNote$$aJournal Article 000042907 3367_ $$2BibTeX$$aARTICLE 000042907 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000042907 3367_ $$2DRIVER$$aarticle 000042907 440_0 $$04924$$aPhysical Review E$$v69$$x1539-3755 000042907 500__ $$aRecord converted from VDB: 12.11.2012 000042907 520__ $$aWe present two classes of improved estimators for mutual information M(X,Y), from samples of random points distributed according to some joint probability density mu(x,y). In contrast to conventional estimators based on binnings, they are based on entropy estimates from k-nearest neighbor distances. This means that they are data efficient (with k=1 we resolve structures down to the smallest possible scales), adaptive (the resolution is higher where data are more numerous), and have minimal bias. Indeed, the bias of the underlying entropy estimates is mainly due to nonuniformity of the density at the smallest resolved scale, giving typically systematic errors which scale as functions of k/N for N points. Numerically, we find that both families become exact for independent distributions, i.e. the estimator (M) over cap (X,Y) vanishes (up to statistical fluctuations) if mu(x,y)=mu(x)mu(y). This holds for all tested marginal distributions and for all dimensions of x and y. In addition, we give estimators for redundancies between more than two random variables. We compare our algorithms in detail with existing algorithms. Finally, we demonstrate the usefulness of our estimators for assessing the actual independence of components obtained from independent component analysis (ICA), for improving ICA, and for estimating the reliability of blind source separation. 000042907 536__ $$0G:(DE-Juel1)FUEK254$$2G:(DE-HGF)$$aBetrieb und Weiterentwicklung des Höchstleistungsrechners$$cI03$$x0 000042907 542__ $$2Crossref$$i2004-06-23$$uhttp://link.aps.org/licenses/aps-default-license 000042907 588__ $$aDataset connected to Web of Science 000042907 650_7 $$2WoSType$$aJ 000042907 7001_ $$0P:(DE-Juel1)VDB46296$$aStögbauer, H.$$b1$$uFZJ 000042907 7001_ $$0P:(DE-Juel1)136887$$aGrassberger, P.$$b2$$uFZJ 000042907 77318 $$2Crossref$$3journal-article$$a10.1103/physreve.69.066138$$bAmerican Physical Society (APS)$$d2004-06-23$$n6$$p066138$$tPhysical Review E$$v69$$x1539-3755$$y2004 000042907 773__ $$0PERI:(DE-600)2844562-4$$a10.1103/PhysRevE.69.066138$$gVol. 69, p. 066138$$n6$$p066138$$q69<066138$$tPhysical review / E$$v69$$x1539-3755$$y2004 000042907 8567_ $$uhttp://hdl.handle.net/2128/2365$$uhttp://dx.doi.org/10.1103/PhysRevE.69.066138 000042907 8564_ $$uhttps://juser.fz-juelich.de/record/42907/files/60015.pdf$$yOpenAccess 000042907 8564_ $$uhttps://juser.fz-juelich.de/record/42907/files/60015.jpg?subformat=icon-1440$$xicon-1440$$yOpenAccess 000042907 8564_ $$uhttps://juser.fz-juelich.de/record/42907/files/60015.jpg?subformat=icon-180$$xicon-180$$yOpenAccess 000042907 8564_ $$uhttps://juser.fz-juelich.de/record/42907/files/60015.jpg?subformat=icon-640$$xicon-640$$yOpenAccess 000042907 909CO $$ooai:juser.fz-juelich.de:42907$$pdnbdelivery$$pVDB$$pdriver$$popen_access$$popenaire 000042907 9131_ $$0G:(DE-Juel1)FUEK254$$bInformation$$kI03$$lWissenschaftliches Rechnen$$vBetrieb und Weiterentwicklung des Höchstleistungsrechners$$x0 000042907 9141_ $$y2004 000042907 915__ $$0StatID:(DE-HGF)0010$$aJCR/ISI refereed 000042907 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess 000042907 9201_ $$0I:(DE-Juel1)NIC-20090406$$gNIC$$kNIC$$lJohn von Neumann - Institut für Computing$$x0 000042907 970__ $$aVDB:(DE-Juel1)60015 000042907 980__ $$aVDB 000042907 980__ $$aJUWEL 000042907 980__ $$aConvertedRecord 000042907 980__ $$ajournal 000042907 980__ $$aI:(DE-Juel1)NIC-20090406 000042907 980__ $$aUNRESTRICTED 000042907 980__ $$aFullTexts 000042907 9801_ $$aFullTexts 000042907 999C5 $$1T. 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