Machine Learning

Chapter 7

Linear Discriminant Analysis

7.1 Introduction

In 1936, statistical pioneer Ronald Fisher discussed linear discriminant [1] that became a common method to be used in statistics, pattern recognition, and machine learning. The idea was to find a linear combination of features that are able to separate two or more classes. The resulting linear combination can also be used for dimensionality reduction. Linear discriminant analysis (LDA) is a generalization of the Fisher linear discriminant.

This method was used to explain the bankruptcy or survival of the firm [2]. In face recognition problems, it is used to reduce dimensions.

LDA seeks to maximize class discrimination and produces exactly as many linear functions as there are classes. ...