The objective of Linear Discriminant Analysis (LDA) is to perform dimensionality reduction while preserving as much of the class discriminatory information as possible
- Assume we have a set of D-dimensional samples {x(1, x(2, …, x(N}, N1 of which belong to class ω1, and N2 to class ω2. We seek to obtain a scalar y by projecting the samples x onto a line
y=wTx
- Of all the possible lines we would like to select the one that maximizes the separability of the scalars
