Abstract
A new kernel discriminant analysis algorithm, called kernel-based enhanced maximum margin criterion (KEMMC), is presented for extracting features from high-dimensional data space. In EMMC, the local property is taken into account so that the data points of neighboring classes can be mapped far away. Moreover, the regularized technique is employed to deal with small sample size problem. It is extended to a nonlinear form by mapping the input space to a high-dimensional feature space that can make the mapped features linearly separable. Extensive experiments demonstrate the effectiveness of the proposed algorithm.
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Zhang, C., Hu, H. (2014). Kernel-Based Enhanced Maximum Margin Criterion Algorithm for High-Dimensional Feature Extraction. In: Long, S., Dhillon, B.S. (eds) Proceedings of the 13th International Conference on Man-Machine-Environment System Engineering. MMESE 2013. Lecture Notes in Electrical Engineering, vol 259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38968-9_22
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DOI: https://doi.org/10.1007/978-3-642-38968-9_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38967-2
Online ISBN: 978-3-642-38968-9
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