LDA works by projecting data points onto a lower-dimensional space in such a way that the separation between different classes is maximized. The key steps involved in LDA include:
Computing the mean vectors for each class. Calculating the within-class and between-class scatter matrices. Solving the generalized eigenvalue problem for these scatter matrices to find the linear discriminants. Projecting the data onto the new feature space defined by these discriminants.