This document discusses Fisher's Linear Discriminant, a statistical dimensionality reduction technique used in machine learning and pattern recognition. It works by maximizing the distance between different classes while minimizing the distance within each class. The document provides an example using a sample 2-class dataset to demonstrate the steps of FLD, which includes calculating within-class and between-class scatter matrices to determine the optimal projection vector. This projects the high-dimensional data onto a line that best separates the two classes. Advantages are minimizing variance between classes and working for multi-class problems, while disadvantages include not handling non-linearity or small sample sizes well.