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dc.contributor.authorLiu, Chien-Liangen_US
dc.contributor.authorHsaio, Wen-Hoaren_US
dc.contributor.authorLee, Chia-Hoangen_US
dc.contributor.authorGou, Fu-Shengen_US
dc.date.accessioned2014-12-08T15:36:50Z-
dc.date.available2014-12-08T15:36:50Z-
dc.date.issued2014-07-01en_US
dc.identifier.issn2168-2267en_US
dc.identifier.urihttp://dx.doi.org/10.1109/TCYB.2013.2278466en_US
dc.identifier.urihttp://hdl.handle.net/11536/25235-
dc.description.abstractThis paper devises a semi-supervised learning method called semi-supervised linear discriminant clustering (Semi-LDC). The proposed algorithm considers clustering and dimensionality reduction simultaneously by connecting K-means and linear discriminant analysis (LDA). The goal is to find a feature space where the K-means can perform well in the new space. To exploit the information brought by unlabeled examples, this paper proposes to use soft labels to denote the labels of unlabeled examples. The Semi-LDC uses the proposed algorithm, called constrained-PLSA, to estimate the soft labels of unlabeled examples. We use soft LDA with hard labels of labeled examples and soft labels of unlabeled examples to find a projection matrix. The clustering is then performed in the new feature space. We conduct experiments on three data sets. The experimental results indicate that the proposed method can generally outperform other semi-supervised methods. We further discuss and analyze the influence of soft labels on classification performance by conducting experiments with different percentages of labeled examples. The finding shows that using soft labels can improve performance particularly when the number of available labeled examples is insufficient to train a robust and accurate model. Additionally, the proposed method can be viewed as a framework, since different soft label estimation methods can be used in the proposed method according to application requirements.en_US
dc.language.isoen_USen_US
dc.subjectClusteringen_US
dc.subjectlinear discriminant analysisen_US
dc.subjectsemi-supervised learningen_US
dc.subjectsoft labelen_US
dc.subjecttext miningen_US
dc.titleSemi-supervised Linear Discriminant Clusteringen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/TCYB.2013.2278466en_US
dc.identifier.journalIEEE TRANSACTIONS ON CYBERNETICSen_US
dc.citation.volume44en_US
dc.citation.issue7en_US
dc.citation.spage989en_US
dc.citation.epage1000en_US
dc.contributor.department交大名義發表zh_TW
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentNational Chiao Tung Universityen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000342225800001-
dc.citation.woscount0-
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