The document discusses principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction in pattern recognition and their application to face recognition. PCA finds the directions along which the data varies the most to reduce dimensionality while retaining variation. LDA seeks directions that maximize between-class variation and minimize within-class variation. Studies show LDA performs better than PCA for classification when the training set is large and representative of each class.