Friday, March 14, 2014

Linear discriminant analysis (slides)

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
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.