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dc.contributor.authorKampffmeyer, Michael C.
dc.contributor.authorLøkse, Sigurd
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorLivi, Lorenzo
dc.contributor.authorSalberg, Arnt Børre
dc.contributor.authorJenssen, Robert
dc.date.accessioned2020-03-16T14:01:01Z
dc.date.available2020-03-16T14:01:01Z
dc.date.issued2019-02-08
dc.description.abstractA promising direction in deep learning research consists in learning representations and simultaneously discovering cluster structure in unlabeled data by optimizing a discriminative loss function. As opposed to supervised deep learning, this line of research is in its infancy, and how to design and optimize suitable loss functions to train deep neural networks for clustering is still an open question. Our contribution to this emerging field is a new deep clustering network that leverages the discriminative power of information-theoretic divergence measures, which have been shown to be effective in traditional clustering. We propose a novel loss function that incorporates geometric regularization constraints, thus avoiding degenerate structures of the resulting clustering partition. Experiments on synthetic benchmarks and real datasets show that the proposed network achieves competitive performance with respect to other state-of-the-art methods, scales well to large datasets, and does not require pre-training steps.en_US
dc.identifier.citationKampffmeyer, M.C.; Løkse, S.; Bianchi, F.M.; Livi, L.; Salberg, A.B.; Jenssen, R. (2019) Deep divergence-based approach to clustering.<i> Neural Networks, 113</i>, 91-101.en_US
dc.identifier.cristinIDFRIDAID 1692898
dc.identifier.doi10.1016/j.neunet.2019.01.015
dc.identifier.issn0893-6080
dc.identifier.issn1879-2782
dc.identifier.urihttps://hdl.handle.net/10037/17759
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofLøkse, S. (2020). Leveraging Kernels for Unsupervised Learning. (Doctoral thesis). <a href=https://hdl.handle.net/10037/19911>https://hdl.handle.net/10037/19911</a>.
dc.relation.journalNeural Networks
dc.rights.accessRightsopenAccessen_US
dc.rights.holder© 2019TheAuthorsen_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Computer technology: 551en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Datateknologi: 551en_US
dc.titleDeep divergence-based approach to clusteringen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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