Why is SVD used for dimensionality reduction instead of eigen decomposition?

SVD works for any matrix, while eigen decomposition requires square matrices.

SVD decomposes any matrix into orthogonal bases and singular values, making it more general and numerically stable. Eigen decomposition only works reliably for square matrices and can fail for non-symmetric cases.