Here are the slides I use for my course about “Linear Discriminant Analysis” (LDA). The two main assumptions which enable to obtain a linear classifier are highlighted. The LDA is very interesting because we can interpret the classifier in different ways: it is a parametric method based on the MAP (maximum a posteriori) decision rule; it is a classifier based on a distance to the conditional centroids; it is a linear separator which defines various regions in the representation space.
Statistical tools for the overall model evaluation and the checking of the relevance of the predictive variables are presented.
Keywords: machine learning, supervised methods, discriminant analysis, predictive discriminant analysis, linear discriminant analysis, linear classification functions, wilks lambda, stepdisc, feature selection
Slides: linear discriminant analysis
References:
J. Gareth, D. Witten, T. Hastie, R. Tibshirani, "An introduction to statistical learning with applications in R", Springer, 2013.
R. Duda, P. Hart, G. Stork, "Pattern Classification", Wiley, 2000.
