Empirical Normalization for Quadratic Discriminant Analysis and Classifying Cancer Subtypes
This method addresses classification challenges in computational biology, such as cancer subtype identification, but appears incremental as it builds on existing quadratic discriminant analysis with a transformation step.
The authors tackled the problem of binary classification by introducing Empirical Discriminant Analysis (EDA), which transforms data to approximate Gaussian distributions using an empirical feature map, enabling the use of standard quadratic discriminants; they applied it to computational biology datasets, but no concrete performance numbers are provided.
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.