PCA is computed on a set of vectors of dimension D = 5 and the eigenvalues computed for the corresponding covariance matrix are 1 = 5, 2 = 4, 3 = 3, 4 = 2 and 5 = 1. How many eigenvectors do we need to keep for reconstruction to get at least 90% of the variance explained?