@inproceedings{960f73363b3f49f7b9dcbf12bc72c4fc,
title = "Asymptotic performance of regularized quadratic discriminant analysis based classifiers",
abstract = "This paper carries out a large dimensional analysis of the standard regularized quadratic discriminant analysis (QDA) classifier designed on the assumption that data arise from a Gaussian mixture model. 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. Under some mild assumptions, we show that the asymptotic classification error converges to a deterministic quantity that depends only on the covariances and means associated with each class as well as the problem dimensions. Such a result permits a better understanding of the performance of regularized QDA and can be used to determine the optimal regularization parameter that minimizes the misclassification error probability. Despite being valid only for Gaussian data, our theoretical findings are shown to yield a high accuracy in predicting the performances achieved with real data sets drawn from popular real data bases, thereby making an interesting connection between theory and practice.",
keywords = "Classification, Deterministic equivalent, Machine learning, QDA, Random matrix theory",
author = "Khalil Elkhalil and Abla Kammoun and Romain Couillet and Al-Naffouri, {Tareq Y.} and Alouini, {Mohamed Slim}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 ; Conference date: 25-09-2017 Through 28-09-2017",
year = "2017",
month = dec,
day = "5",
doi = "10.1109/MLSP.2017.8168172",
language = "English (US)",
series = "IEEE International Workshop on Machine Learning for Signal Processing, MLSP",
publisher = "IEEE Computer Society",
pages = "1--6",
editor = "Naonori Ueda and Jen-Tzung Chien and Tomoko Matsui and Jan Larsen and Shinji Watanabe",
booktitle = "2017 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2017 - Proceedings",
address = "United States",
}