Abstract
A joint clustering and classification approach is proposed. This approach
exploits unlabeled data for efficient clustering, which is applied in the
classification with support vector machine (SVM) in the case of small-size training
samples. The proposed method requires no prior information on data labels, and
yields better cluster structures. Through cluster assumption and the notions of
support vectors, the most confident k cluster centers and data points near the
cluster boundaries are labeled and used to train a reliable SVM classifier. Our
method gains better estimation of data distributions and mitigates the
unrepresentative problem of small-size training samples. The data set collected from
Landsat Thematic Mapper (Landsat TM-5) validates the effectiveness of the proposed
approach.
© 2011 Chinese Optics Letters
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