Abstract
In this paper, to deal with the problem that nearest neighbor graph is hard to be connected in the original Isomap, a new supervised Isomap method (SS-Isomap) with adaptive parameters is proposed. This method considers the density of intra-class data points and proposes an adaptive function. The experiment results based on UCI datasets show that SS-Isomap has a better discriminant ability.
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Acknowledgment
This work was supported by the National Natural Science Foundation of China (Grant no. 61273303 and no. 61572381). The authors would like to thank all the editors and reviewers for their valuable comments and suggestions.
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Feng, Z., Li, B. (2018). An Improved Supervised Isomap Method Using Adaptive Parameters. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_27
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DOI: https://doi.org/10.1007/978-3-319-95957-3_27
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