A Large Dimensional Study of Regularized Discriminant Analysis Classifiers

1 Nov 2017Khalil ElkhalilAbla KammounRomain CouilletTareq Y. Al-NaffouriMohamed-Slim Alouini

This article carries out a large dimensional analysis of standard regularized discriminant analysis classifiers designed on the assumption that data arise from a Gaussian mixture model with different means and covariances. The analysis relies on fundamental results from random matrix theory (RMT) when both the number of features and the cardinality of the training data within each class grow large at the same pace... (read more)

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