Abstract
Hybrid discriminant analysis (HDA) combining principal component analysis (PCA) with linear discriminant analysis (LDA) can achieve better performance for samples following complex distribution. However, HDA can not work well for complex and nonlinear distributed data. As a result, a locality preserving HAD (LPKHDA) algorithm is proposed by combining the kernel method with manifold learning, overcoming the shortcomings of manifold learning and kernel methods. According to kernel-induced selection criterion, the optimal kernel parameter of LPKHDA can be achieved efficiently through gradient method and a boosted LPKHDA algorithm based on Adaboost idea is implemented. Extensive experiments are conducted to evaluate the proposed algorithm.
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Ren, S., Liu, X., Yang, M., Xu, G. (2012). Locality Preserving Kernel Hybrid Discriminate Analysis for Dimensional Reduction. In: Wang, F.L., Lei, J., Lau, R.W.H., Zhang, J. (eds) Multimedia and Signal Processing. CMSP 2012. Communications in Computer and Information Science, vol 346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35286-7_3
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DOI: https://doi.org/10.1007/978-3-642-35286-7_3
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