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arxiv 1906.02590 v1 pith:LVAATH53 submitted 2019-06-01 stat.ML cs.LG

Linear and Quadratic Discriminant Analysis: Tutorial

Benyamin Ghojogh , Mark Crowley This is my paper
classification stat.ML cs.LG
keywords analysisdiscriminantbayeslearninglinearquadraticthentutorial
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This tutorial explains Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) as two fundamental classification methods in statistical and probabilistic learning. We start with the optimization of decision boundary on which the posteriors are equal. Then, LDA and QDA are derived for binary and multiple classes. The estimation of parameters in LDA and QDA are also covered. Then, we explain how LDA and QDA are related to metric learning, kernel principal component analysis, Mahalanobis distance, logistic regression, Bayes optimal classifier, Gaussian naive Bayes, and likelihood ratio test. We also prove that LDA and Fisher discriminant analysis are equivalent. We finally clarify some of the theoretical concepts with simulations we provide.

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Cited by 4 Pith papers

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