What is Linear Discriminant Analysis (LDA)?
Updated May 16, 2026
Short answer
LDA is a supervised dimensionality reduction method that maximizes class separability.
Deep explanation
Unlike PCA, LDA uses class labels to maximize between-class variance and minimize within-class variance, making it useful for classification tasks.
Real-world example
Face recognition systems use LDA for class separation.
Common mistakes
- Using LDA without labels.
Follow-up questions
- How many components can LDA produce?
- When is LDA better than PCA?