, d >> n. In this situation, matlab set of rules defined in old sections develop into impractical. We would decide on engineering run time that relies upon only on matlab variety of training examples n, or that at least has engineering decreased dependence on n. Note that during matlab SVD factorization X = U Sigma V^T , matlab eigenvectors in U equivalent to non zero singular values in Sigma square roots of eigenvalues are in engineering one to one correspondence with matlab eigenvectors in V . After performing dimensionality reduction on U and preserving only matlab first l eigenvectors, corresponding to matlab top l non zero singular values in Sigma , these eigenvectors will nonetheless be in engineering one to at least one correspondence with matlab first l eigenvectors in V : Sigma is square and invertible, as a result of its diagonal has non zero entries. Thus, matlab following conversion between matlab top l eigenvectors will also be derived:Fisher Discriminant Analysis FDA is sometimes called Fisher Linear Discriminant Analysis FLDA or simply Linear Discriminant Analysis LDA.