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Ranking Using Transition Probabilities Learned from Multi-Attribute Data

dc.contributor.authorLøkse, Sigurd
dc.contributor.authorJenssen, Robert
dc.date.accessioned2019-04-09T10:40:03Z
dc.date.available2019-04-09T10:40:03Z
dc.date.issued2018-09-13
dc.description.abstractIn this paper, as a novel approach, we learn Markov chain transition probabilities for ranking of multi-attribute data from the inherent structures in the data itself. The procedure is inspired by consensus clustering and exploits a suitable form of the PageRank algorithm. This is very much in the spirit of the original PageRank utilizing the hyperlink structure to learn such probabilities. As opposed to existing approaches for ranking multi-attribute data, our method is not dependent on tuning of critical user-specified parameters. Experiments show the benefits of the proposed method.en_US
dc.description.sponsorshipNVIDIA Corporationen_US
dc.descriptionAccepted manuscript. Published version available at <a href=https://doi.org/10.1109/ICASSP.2018.8462132>https://doi.org/10.1109/ICASSP.2018.8462132. </a> ©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.identifier.citationLøkse, S. & Jenssen, R. (2018). Ranking Using Transition Probabilities Learned from Multi-Attribute Data. <i>Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing</i>, 15. - 20. April 2018, p. 2851-2855. https://doi.org/10.1109/ICASSP.2018.8462132en_US
dc.identifier.cristinIDFRIDAID 1628392
dc.identifier.doi10.1109/ICASSP.2018.8462132
dc.identifier.issn1520-6149
dc.identifier.urihttps://hdl.handle.net/10037/15180
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.journalProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/IKTPLUSS/239844/Norway/Next Generation Kernel-Based Machine Learning for Big Missing Data Applied to Earth Observation//en_US
dc.rights.accessRightsopenAccessen_US
dc.subjectRankingen_US
dc.subjectmulti-attribute dataen_US
dc.subjecttransition probabilitiesen_US
dc.subjectsimilarity measureen_US
dc.subjectparameter freeen_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleRanking Using Transition Probabilities Learned from Multi-Attribute Dataen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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