dc.contributor.authorNie, Feiping
dc.contributor.authorXu, Dong
dc.contributor.authorLi, Xuelong
dc.date.accessioned2013-07-15T06:51:57Z
dc.date.available2013-07-15T06:51:57Z
dc.date.copyright2011en_US
dc.date.issued2011
dc.identifier.citationNie, F., Xu, D, & Li, X. (2012). Initialization Independent Clustering With Actively Self-Training Method. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 42(1), 17-27.en_US
dc.identifier.issn1083-4419en_US
dc.identifier.urihttp://hdl.handle.net/10220/11431
dc.description.abstractThe results of traditional clustering methods are usually unreliable as there is not any guidance from the data labels, while the class labels can be predicted more reliable by the semisupervised learning if the labels of partial data are given. In this paper, we propose an actively self-training clustering method, in which the samples are actively selected as training set to minimize an estimated Bayes error, and then explore semisupervised learning to perform clustering. Traditional graph-based semisupervised learning methods are not convenient to estimate the Bayes error; we develop a specific regularization framework on graph to perform semisupervised learning, in which the Bayes error can be effectively estimated. In addition, the proposed clustering algorithm can be readily applied in a semisupervised setting with partial class labels. Experimental results on toy data and real-world data sets demonstrate the effectiveness of the proposed clustering method on the unsupervised and the semisupervised setting. It is worthy noting that the proposed clustering method is free of initialization, while traditional clustering methods are usually dependent on initialization.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesIEEE transactions on systems, man, and cybernetics, part b (cybernetics)en_US
dc.rights© 2011 IEEE.en_US
dc.subjectDRNTU::Engineering::Computer science and engineering
dc.titleInitialization independent clustering with actively self-training methoden_US
dc.typeJournal Article
dc.contributor.schoolSchool of Computer Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1109/TSMCB.2011.2161607


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