Empirical normalization for quadratic discriminant analysis and classifying cancer subtypes
Kon, Mark A.
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Citation (published version)M.A. Kon, N. Nikolaev. 2011. "Empirical Normalization for Quadratic Discriminant Analysis and Classifying Cancer Subtypes." 2011 10th International Conference on Machine Learning and Applications and Workshops, https://doi.org/10.1109/icmla.2011.160
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.