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. © 2011 Springer-Verlag.
CITATION STYLE
Zhu, L. (2011). Optimal regularization parameter estimation for regularized discriminant analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6838 LNCS, pp. 77–82). https://doi.org/10.1007/978-3-642-24728-6_11
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