Machine Learning, Linear Algebra.
D={x_1,…,x_N} where each point has p dimensions (x_n∈R^p), find a low-dimensional representation of the original dataset which has k<p dimensions y_n∈R^k So how to do that? Just follow these steps: Perform Singular Value Decomposition(SVD) to obtain the Eigenvectors and Eigenvalues the covariance