Abstract
Regularized linear discriminant analysis (RLDA) is a popular LDA-based method for dimension reduction. Despite its good performance, how to choose the parameter of the regularizer efficiently is still unanswered, especially for multi-class situation. In this paper, we first prove that regularizing LDA is equivalent to augmenting the training set in a specific way and thereby propose an efficient model selection criterion based on the principle of maximum information preservation, extensive experiments prove the usefulness and efficiency of our method.
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Zhu, L. (2011). Optimal Regularization Parameter Estimation for Regularized Discriminant Analysis. In: Huang, DS., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing. ICIC 2011. Lecture Notes in Computer Science, vol 6838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24728-6_11
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DOI: https://doi.org/10.1007/978-3-642-24728-6_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24727-9
Online ISBN: 978-3-642-24728-6
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