Concept
Fisher's Criterion is a method used in linear discriminant analysis to find a linear combination of features that separates two or more classes of objects or events. It maximizes the ratio of the variance between the classes to the variance within the classes, thus enhancing class separability in a dataset.
Concept
Linear Discriminant Analysis (LDA) is a dimensionality reduction technique used in supervised learning to project data onto a lower-dimensional space while maximizing class separability. It is particularly effective for classification tasks where the goal is to find a linear combination of features that best separates two or more classes.
3