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dc.contributor.authorMikalsen, Karl Øyvind
dc.contributor.authorSoguero-Ruiz, Cristina
dc.contributor.authorBianchi, Filippo Maria
dc.contributor.authorJenssen, Robert
dc.date.accessioned2020-01-20T14:13:11Z
dc.date.available2020-01-20T14:13:11Z
dc.date.issued2019-01-29
dc.description.abstractNoisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not accounted for. How to fully utilize noisy labels has been studied extensively within the framework of standard supervised machine learning over a period of several decades. However, very little research has been conducted on solving the challenge posed by noisy labels in non-standard settings. This includes situations where only a fraction of the samples are labeled (semi-supervised) and each high-dimensional sample is associated with multiple labels. In this work, we present a novel semi-supervised and multi-label dimensionality reduction method that effectively utilizes information from both noisy multi-labels and unlabeled data. With the proposed Noisy <i>multi-label semi-supervised dimensionality reduction (NMLSDR)</i> method, the noisy multi-labels are denoised and unlabeled data are labeled simultaneously via a specially designed label propagation algorithm. NMLSDR then learns a projection matrix for reducing the dimensionality by maximizing the dependence between the enlarged and denoised multi-label space and the features in the projected space. Extensive experiments on synthetic data, benchmark datasets, as well as a real-world case study, demonstrate the effectiveness of the proposed algorithm and show that it outperforms state-of-the-art multi-label feature extraction algorithms.en_US
dc.identifier.citationMikalsen KØ, Soguero-Ruiz C, Bianchi FM, Jenssen R. Noisy multi-label semi-supervised dimensionality reduction. Pattern Recognition. 2019;90:257-270en_US
dc.identifier.cristinIDFRIDAID 1701296
dc.identifier.doi10.1016/j.patcog.2019.01.033
dc.identifier.issn0031-3203
dc.identifier.issn1873-5142
dc.identifier.urihttps://hdl.handle.net/10037/17151
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.journalPattern Recognition
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.rights.holderCopyright 2019 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400en_US
dc.titleNoisy multi-label semi-supervised dimensionality reductionen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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