Abstract
In this paper, we propose a novel supervised classification algorithm named Supervised Isometric Mapping Based classification Algorithm (SIMBA). The main idea of SIMBA is to integrate the supervised information into the well-known ISOmetric MAPping (ISOMAP) manifold learning algorithm and classify the transformed data in a low-dimensional feature space. By virtue of the integrated supervised information, the manifold mapping becomes more discriminative, thus the classification performance can be improved. SIMBA can deal with complex high-dimensional data lying on an intrinsically low-dimensional manifold, but only has one free parameter, which is the number of nearest neighbors. Sufficient experiment results demonstrate that SIMBA shows higher classification accuracy on real-world datasets than the state-of-the-art support vector machine classifier.
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Acknowledgement
This research was supported in part by the Chinese National Natural Science Foundation under Grant nos. 61402395, 61472343 and 61379066, Natural Science Foundation of Jiangsu Province under contracts BK20151314, BK20140492 and BK20130452, Natural Science Foundation of Education Department of Jiangsu Province under contract 13KJB520026, and the New Century Talent Project of Yangzhou University.
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He, P., Jing, T., Xu, X., Zhang, L., Lin, H. (2016). Supervised Isometric Mapping Based Classification Algorithm. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_33
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DOI: https://doi.org/10.1007/978-3-319-46257-8_33
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