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Semi-supervised learning for hierarchically structured networks
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dc.contributor.authorKim, Myungjun-
dc.contributor.authorLee, Dong gi-
dc.contributor.authorShin, Hyunjung-
dc.date.issued2019-11-01-
dc.identifier.issn0031-3203-
dc.identifier.urihttps://aurora.ajou.ac.kr/handle/2018.oak/30772-
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067493531&origin=inward-
dc.description.abstractA set of data can be obtained from different hierarchical levels in diverse domains, such as multi-levels of genome data in omics, domestic/global indicators in finance, ancestors/descendants in phylogenetics, genealogy, and sociology. Such layered structures are often represented as a hierarchical network. If a set of different data is arranged in such a way, then one can naturally devise a network-based learning algorithm so that information in one layer can be propagated to other layers through interlayer connections. Incorporating individual networks in layers can be considered as an integration in a serial/vertical manner in contrast with parallel integration for multiple independent networks. The hierarchical integration induces several problems on computational complexity, sparseness, and scalability because of a huge-sized matrix. In this paper, we propose two versions of an algorithm, based on semi-supervised learning, for a hierarchically structured network. The naïve version utilizes existing method for matrix sparseness to solve label propagation problems. In its approximate version, the loss in accuracy versus the gain in complexity is exploited by providing analyses on error bounds and complexity. The experimental results show that the proposed algorithms perform well with hierarchically structured data, and, outperform an ordinary semi-supervised learning algorithm.-
dc.description.sponsorshipThis work was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (no. 2018S1A5B6075104 ), Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (no. 2018-0-00440 , ICT-based Crime Risk Prediction and Response Platform Development for Early Awareness of Risk Situation), and the Ajou University research fund.-
dc.language.isoeng-
dc.publisherElsevier Ltd-
dc.subject.meshHierarchical graphs-
dc.subject.meshHierarchical level-
dc.subject.meshHierarchical network-
dc.subject.meshIndividual network-
dc.subject.meshNetwork-based learning-
dc.subject.meshParallel integration-
dc.subject.meshSemi- supervised learning-
dc.subject.meshStructured networks-
dc.titleSemi-supervised learning for hierarchically structured networks-
dc.typeArticle-
dc.citation.endPage200-
dc.citation.startPage191-
dc.citation.titlePattern Recognition-
dc.citation.volume95-
dc.identifier.bibliographicCitationPattern Recognition, Vol.95, pp.191-200-
dc.identifier.doi10.1016/j.patcog.2019.06.009-
dc.identifier.scopusid2-s2.0-85067493531-
dc.identifier.urlwww.elsevier.com/inca/publications/store/3/2/8/-
dc.subject.keywordHierarchical graph integration-
dc.subject.keywordHierarchical networks-
dc.subject.keywordHierarchically structured networks-
dc.subject.keywordSemi-supervised learning-
dc.type.otherArticle-
dc.description.isoafalse-
dc.subject.subareaSoftware-
dc.subject.subareaSignal Processing-
dc.subject.subareaComputer Vision and Pattern Recognition-
dc.subject.subareaArtificial Intelligence-
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