Fisher's linear discriminant

(1.1 hours to learn)


Fisher's linear discriminant is a technique for visualizing high-dimensional data belonging to multiple classes by projecting it onto a low-dimensional subspace. The subspace is chosen to maximize the ratio of between-class to within-class variance.


This concept has the prerequisites:


  • Derive the subspace which maximizes the ratio of between-class and within-class variance.
  • Why might this projection give better classification results than GDA in the original space?

Core resources (read/watch one of the following)


The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Location: Section 4.3.3, "Reduced-rank linear discriminant analysis," pages 113-119
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


See also