  We introduce a new discriminant analysis method (Empirical Discriminant
Analysis or EDA) for binary classification in machine learning. Given a dataset
of feature vectors, this method defines an empirical feature map transforming
the training and test data into new data with components having Gaussian
empirical distributions. This map is an empirical version of the Gaussian
copula used in probability and mathematical finance. The purpose is to form a
feature mapped dataset as close as possible to Gaussian, after which standard
quadratic discriminants can be used for classification. We discuss this method
in general, and apply it to some datasets in computational biology.
