Optimal regularization parameter estimation for regularized discriminant analysis

Authors:
Lin Zhu
Affiliations:
University of Science and Technology of China, Hefei, Anhui, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

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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.