What are the differences between PCA and LDA?

What are the differences between PCA and LDA?

PCA (Principal Component Analysis) is a technique used for dimensionality reduction, focusing on maximizing variance. LDA (Linear Discriminant Analysis), on the other hand, is used for classification and aims to maximize the separation between classes.

Can you explain how PCA works?

Sure! PCA works by identifying the directions (principal components) in which the data varies the most. It then projects the data onto these directions to reduce dimensionality while retaining as much variance as possible.

What about LDA? How does it differ in approach?

LDA works by finding a linear combination of features that best separates two or more classes. It focuses on maximizing the distance between the means of different classes while minimizing the variance within each class.

When should I use PCA instead of LDA?

Use PCA when you want to reduce dimensionality without considering class labels, and use LDA when you need to classify data and want to maximize class separability.