Document worth reading: “Linear and Quadratic Discriminant Analysis: Tutorial”

This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two elementary classification methods in statistical and probabilistic finding out. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and a lot of classes. The estimation of parameters in LDA and QDA are moreover lined. Then, we make clear how LDA and QDA are related to metric finding out, kernel principal half analysis, Mahalanobis distance, logistic regression, Bayes optimum classifier, Gaussian naive Bayes, and chance ratio verify. We moreover present that LDA and Fisher discriminant analysis are equal. We lastly clarify a lot of the theoretical concepts with simulations we provide. Linear and Quadratic Discriminant Analysis: Tutorial