document: Regularized discriminant analysis for high dimensional , low sample size data Linear and Quadratic Discriminant Analysis have been used widely in many areas of data mining , machine learning , and bioinformatics . Friedman proposed a compromise between Linear and Quadratic Discriminant Analysis , called Regularized Discriminant Analysis ( RDA ) , which has been shown to be more flexible in dealing with various class distributions . RDA applies the regularization techniques by employing two regularization parameters , which are chosen to jointly maximize the classification performance . The optimal pair of parameters is commonly estimated via cross-validation from a set of candidate pairs . It is computationally prohibitive for high dimensional data , especially when the candidate set is large , which limits the applications of RDA to low dimensional data . In this paper , a novel algorithm for RDA is presented for high dimensional data . It can estimate the optimal regularization parameters from a large set of parameter candidates efficiently . Experiments on a variety of datasets confirm the claimed theoretical estimate of the efficiency , and also show that , for a properly chosen pair of regularization parameters , RDA performs favorably in classification , in comparison with other existing classification methods .
doc_bio_tags: O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O