NDLI: Clustering support vector machines for unlabeled data classification

Content Provider	IEEE Xplore Digital Library
Author	Juanying Xie Chunxia Wang Yan Zhang Shuai Jiang
Copyright Year	2009
Description	Author affiliation: School of Computer Science, Shaanxi Normal University, Xian, China (Juanying Xie; Chunxia Wang; Yan Zhang; Shuai Jiang)
Abstract	Clustering support vector machines (CSVM) is proposed in this paper for unlabeled data classification. It is often for us to deal with a large number of data which are wholly unlabeled, e.g., classifying them, and it is impractical for us to label these data manually. Clustering algorithms can be used to generate labels for this kind of data. The global k-means clustering algorithm, the fast global k-means algorithm and another global k-means clustering algorithm using k-d trees are combined respectively with the statistical method F-distribution in our paper to generate labels for those wholly unlabeled data, and then the labeled data are trained with SVM for classification. Our proposed approach (CSVM) is tested on four different synthetically generated data sets, which was wholly unlabeled. The experiment results show that our CSVM is efficient to classify the wholly unlabeled data.
Starting Page	34
Ending Page	38
File Size	1120216
Page Count	5
File Format	PDF
ISBN	9781424446995
DOI	10.1109/ICTM.2009.5413037
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-05
Publisher Place	Hong Kong
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Statistical analysis Pattern recognition Statistics K-means clustering Support vector machines Computer science Degradation K-d trees Support vector machine classification Clustering algorithms Machine learning Fast gobal k-means clustering Testing Classification tree analysis Global k-means custering
Content Type	Text
Resource Type	Article

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