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dc.contributor.authorMøllersen, Kajsa
dc.contributor.authorHardeberg, Jon Yngve
dc.contributor.authorGodtliebsen, Fred
dc.date.accessioned2020-07-02T08:55:50Z
dc.date.available2020-07-02T08:55:50Z
dc.date.issued2020-06-26
dc.description.abstractMulti-instance (MI) learning is a branch of machine learning, where each object (bag) consists of multiple feature vectors (instances)—for example, an image consisting of multiple patches and their corresponding feature vectors. In MI classification, each bag in the training set has a class label, but the instances are unlabeled. The instances are most commonly regarded as a set of points in a multi-dimensional space. Alternatively, instances are viewed as realizations of random vectors with corresponding probability distribution, where the bag is the distribution, not the realizations. By introducing the probability distribution space to bag-level classification problems, dissimilarities between probability distributions (divergences) can be applied. The bag-to-bag Kullback–Leibler information is asymptotically the best classifier, but the typical sparseness of MI training sets is an obstacle. We introduce bag-to-class divergence to MI learning, emphasizing the hierarchical nature of the random vectors that makes bags from the same class different. We propose two properties for bag-to-class divergences, and an additional property for sparse training sets, and propose a dissimilarity measure that fulfils them. Its performance is demonstrated on synthetic and real data. The probability distribution space is valid for MI learning, both for the theoretical analysis and applications.en_US
dc.identifier.citationMøllersen K, Hardeberg JY, Godtliebsen F. A Probabilistic Bag-to-Class Approach to Multiple-Instance Learning . Data. 2020;5(2)en_US
dc.identifier.cristinIDFRIDAID 1818083
dc.identifier.doihttps://doi.org/10.3390/data5020056
dc.identifier.issn2306-5729
dc.identifier.urihttps://hdl.handle.net/10037/18747
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalData
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801en_US
dc.subjectVDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801en_US
dc.titleA Probabilistic Bag-to-Class Approach to Multiple-Instance Learningen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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