Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43778
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dc.contributorSchool of Nursingen_US
dc.creatorWang, Gen_US
dc.creatorChoi, KSen_US
dc.creatorDeng, Zen_US
dc.date.accessioned2016-06-07T06:23:17Z-
dc.date.available2016-06-07T06:23:17Z-
dc.identifier.issn1064-1246 (Print)en_US
dc.identifier.urihttp://hdl.handle.net/10397/43778-
dc.language.isoenen_US
dc.publisherIOS Pressen_US
dc.rightsCopyright held by the author(s)en_US
dc.rightsG. Wang, K.S. Choi, Z. Deng. Noise-benefit FRSDE for Speedup of Density Estimation on Large Data. Journal of Intelligent and Fuzzy Systems, vol. 30, no. 1, pp. 443-450, 9 Sep 2016. The final publication is available at IOS Press through http://dx.doi.org/10.3233/IFS-151768.en_US
dc.subjectCore seten_US
dc.subjectFast reduced set density estimatoren_US
dc.subjectMinimal enclosing ballen_US
dc.subjectNB-FRSDEen_US
dc.subjectNoise-benefiten_US
dc.titleNoise-benefit FRSDE for speedup of density estimation on large dataen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage443en_US
dc.identifier.epage450en_US
dc.identifier.volume30en_US
dc.identifier.issue1en_US
dc.identifier.doi10.3233/IFS-151768en_US
dcterms.abstractFast Reduced set density estimator (FRSDE) is an important technique to realize the fast kernel density estimation based on the fast minimal enclosing ball (MEB) approximation technique. However, its performance on the running time is severely affected by the approximation parameter ε used in this algorithm, where a smaller value will lead to more accurate approximation but heavy learning burden. In this study, we reveal that the random Gaussian white noise manually added to the data will speed up the learning and accordingly propose a speedup version of FRSDE, i.e., the noise-benefit FRSDE (NB-FRSDE). NB-FRSDE can realize such a speedup because a larger value of ε can be used on the noisy version of the original data to obtain the equivalent approximation performance, which only can be obtained by FRSDE on the original data with a smaller value of ε. The distinctive characteristics of NB-FRSDE exist in the following aspects: (1) its implementation is very simple because NB-FRSDE is the same as FRSDE except that there are Gaussian noises manually added to the original data in NB-FRSDE. (2) While most of the existing machine learning methods always try to remove the noise in order to overcome the influence of noise, NB-FRSDE benefits from the manually added noise in the sense of the average running time. The experimental studies on density estimation and its application to image segmentation demonstrate the above advantages.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationJournal of intelligent and fuzzy systems, 2016, v. 30, no. 1, p. 443-450en_US
dcterms.isPartOfJournal of intelligent and fuzzy systemsen_US
dcterms.issued2016-
dc.identifier.isiWOS:000370279600038-
dc.identifier.scopus2-s2.0-84954522608-
dc.identifier.rosgroupid2015000962-
dc.description.ros2015-2016 > Academic research: refereed > Publication in refereed journalen_US
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumbera0597-n08-
dc.identifier.SubFormID447-
dc.description.fundingSourceRGCen_US
dc.description.fundingTextPolyU5134/12Een_US
dc.description.pubStatusPublisheden_US
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