Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/796
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dc.contributor.authorChreiky, Roberten_US
dc.contributor.authorDelamire, Gillesen_US
dc.contributor.authorDorerr, Clementen_US
dc.contributor.authorPuigt, Matthieuen_US
dc.contributor.authorRoussel , Gillesen_US
dc.contributor.authorAbche, Antoineen_US
dc.date.accessioned2020-12-23T08:37:18Z-
dc.date.available2020-12-23T08:37:18Z-
dc.date.issued2016-
dc.identifier.urihttps://scholarhub.balamand.edu.lb/handle/uob/796-
dc.description.abstractSource apportionment is a very challenging topic for which non-negative source separation is well-suited. Recently, we proposed several informed Non-negative Matrix Factorization (NMF) for which some expert knowledge was introduced. These methods were all dealing with some set values of one factor together with the row sum-to-one property by either processing each constraint alternatingly or using a new parameterization which involves all of them. However, this last method was sensitive to the presence of outliers. In this paper, we thus propose a new robust informed Split Gradient NMF method which is based on a weighted αβ-divergence cost function. Experiments conducted for several input SNR with and without outliers on simulated mixtures of particulate matter sources show the relevance of the new approach.en_US
dc.format.extent6 p.en_US
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.subjectMatrix algebraen_US
dc.subjectComputational geometryen_US
dc.subjectGradient methodsen_US
dc.titleRobust informed split gradient NMF using Alpha ß-divergence for source apportionmenten_US
dc.typeConference Paperen_US
dc.relation.conferenceIEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP) (13-16 Sept. 2016 : Vietri sul Mare, Italy)en_US
dc.contributor.affiliationDepartment of Electrical Engineeringen_US
dc.description.startpage1en_US
dc.description.endpage6en_US
dc.date.catalogued2018-05-18-
dc.description.statusPublisheden_US
dc.identifier.ezproxyURLhttp://ezsecureaccess.balamand.edu.lb/login?url=https://ieeexplore.ieee.org/document/7738843/en_US
dc.identifier.OlibID180395-
dc.relation.ispartoftextIEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP), 2016en_US
dc.provenance.recordsourceOliben_US
Appears in Collections:Department of Electrical Engineering
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