This problem goes through a simple numerical example to help you get familiar with PCA: Given a data set x = {x() R}^_ of N points in a two-dimensional feature space with sample covariance matrix given by l=1 - [ C = 6 2 26 " we perform principal component analysis (PCA) to obtain the principal components. If we use the two principal components with the largest eigenvalues obtained by PCA to project X linearly onto another space spanned by the principal components, what is the maximum percentage of total variance that can be explained by the two principal components together? Justify your answer.