Linear Discriminant Analysis

Linear Discriminant Analysis is a classical method that can be used both for classification and dimensionality reduction. Its core idea is to find linear combinations that maximize separation between classes while minimizing within-class variance. It can perform strongly on low- to medium-dimensional datasets. However, its distributional assumptions and sensitivity to covariance structure should be considered carefully.