Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/96337
Title: Initialization independent clustering with actively self-training method
Authors: Nie, Feiping
Xu, Dong
Li, Xuelong
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2011
Source: Nie, 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.
Series/Report no.: IEEE transactions on systems, man, and cybernetics, part b (cybernetics)
Abstract: The 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.
URI: https://hdl.handle.net/10356/96337
http://hdl.handle.net/10220/11431
ISSN: 1083-4419
DOI: 10.1109/TSMCB.2011.2161607
Rights: © 2011 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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