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 this paper, the EMMC is firstly proposed which attempts to maximize the average margin between classes after dimensionality reduction transformation. In our method, a weighted matrix is introduced and 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. As EMMC is a linear method, it is extended to a nonlinear form by mapping the input space to a high-dimensional feature space which can make the mapped features linearly separable. Extensive experiments on handwritten digit image and face image data demonstrate the effectiveness of the proposed algorithm.
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Hu, H. (2012). Kernel Based Enhanced Maximum Margin Criterion for Feature Extraction. In: Zheng, WS., Sun, Z., Wang, Y., Chen, X., Yuen, P.C., Lai, J. (eds) Biometric Recognition. CCBR 2012. Lecture Notes in Computer Science, vol 7701. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35136-5_42
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DOI: https://doi.org/10.1007/978-3-642-35136-5_42
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