Linear Discriminant Analysis (LDA)

Linear Discriminant Analysis (LDA) is a dimensionality reduction technique that projects data into a subspace optimized for class separability. The algorithm identifies basis vectors that maximize inter-class variance while minimizing intra-class variance within the projected subspace. Implementation typically involves computing scatter matrices and solving a generalized eigenvalue problem to determine the optimal projection axes.