Work

Empirical Normalization for Quadratic Discriminant Analysis and Classifying Cancer Subtypes

Year: 2011

Type: conference-paper

Abstract: 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.

Source: International Conference on Machine Learning and Applications

DOI: 10.1109/icmla.2011.160

Authors: M. A. Kon, N. Nikolaev

Institution: Boston University

Language: English

Topic: Statistical Methods and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funder: National Institutes of Health

OpenAlex ID: W1970506074

FWCI: 0.893

Cites: 27

Cited by: 6

Related to: 10