A statistical method used for dimensionality reduction and feature extraction, Linear Discriminant Analysis (LDA) has applications in pattern recognition and classification. In order to do LDA, a dataset’s numerous classes or groups are best separated by a linear combination of features.
Fundamentally, the goal of LDA is to optimize the ratio of variance within a class to variance between classes. Put another way, it looks for a way to project the data into a lower-dimensional space that maximizes the variance across classes while minimizes the variance within them. Features that emphasize class separability are transformed as a result of this process.