Abstract
In this paper, we put forward a novel supervised feature extraction method based on the Linear Discrimination Analysis: Local Maximum Discrimination Analysis. Also, in order to strengthen the local learning ability of the algorithm and improve the capable of reducing dimensionality, we introduce the Local Weighted Mean to the algorithm LMDA. Better is that, there is no Small Sample Size Problem in the new proposed algorithm with the introduction of Maximum Margin Criterion. In the end, experimental results demonstrate the above advantages of the algorithm LMDA.
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References
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (1986)
Li, R.-H., Liang, S., Chan, E.: Equivalence between LDA/QR and direct LDA. J. International Journal of Cognitive Informatics and Natural Intelligence 5(1), 94–112 (2011)
Yang, L.-P., Gu, X.-H., Ye, H.-W.: Sample locality preserving discriminant analysis for classification. J. Optics and Precision Engineering 19(9), 2205–2213 (2011)
Shu, X., Gao, Y., Lu, H.: Efficient linear discriminant analysis with locality preserving for face recognition. J. Pattern Recognition 45(5), 1892–1898 (2012)
Cui, Y., Fan, L.: Feature extraction using fuzzy maximum margin criterion. J. Neuro. Computing 86(1), 52–58 (2012)
Wan, M., Lai, Z., Jin, Z.: Feature extraction using two-dimensional local graph embedding based on maximum margin criterion. J. Applied Mathematics and Computation 217(23), 9659–9668 (2011)
Vanpanik, V.: Statistical Learning Theory. Wiley, New York (1998)
Lou, S., Zhang, G., Pan, H., Wang, Q.: Supervised Laplacian discriminant analysis for small sample size problem with its application to face recognition. J. Computer Research and Development 49(8), 1730–1737 (2012)
Wong, W.K., Zhao, H.T.: Supervised optimal locality preserving projection. J. Pattern Recognition 45(1), 186–197 (2012)
Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning. J. Artificial Intelligence Review 11(1-5), 75–113 (1997)
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Gao, J. (2013). LMDA: Local Maximum Discrimination Analysis. In: Sun, C., Fang, F., Zhou, ZH., Yang, W., Liu, ZY. (eds) Intelligence Science and Big Data Engineering. IScIDE 2013. Lecture Notes in Computer Science, vol 8261. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-42057-3_55
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DOI: https://doi.org/10.1007/978-3-642-42057-3_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-42056-6
Online ISBN: 978-3-642-42057-3
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