Abstract
Neighbourhood component analysis (NCA) is a method for learning a distance metric which can maximize the classification performance of the K nearest neighbour (KNN) classifier. However, NCA suffers from the small size sample problem that the number of samples is much less than the number of features. To remedy this, this paper proposes a hidden space neighbourhood components analysis (HSNCA), which is a nonlinear extension of NCA. HSNCA first maps the data in the original space into a feature space by a set of nonlinear mapping functions, and then performs NCA in the feature space. Notably, the number of samples is equal to the number of features in the feature space. Thus, HSNCA can avoid the small size sample problem. Experimental results on DNA array datasets show that HSNCA is feasibility and efficiency.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093, and 61402310, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 13KJA520001, and by the Soochow Scholar Project.
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Zhang, L., Huang, X., Wang, B., Li, F., Zhang, Z. (2016). Hidden Space Neighbourhood Component Analysis for Cancer Classification. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_6
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