Machine Learning, Tutorial, Dimensionality Reduction, Deep Learning, Python.
matrix W from the top two eigenvectors. Using the projection matrix, we can now transform a sample x (represented as a 1 x 13-dimensional row vector) onto the PCA subspace (the principal components one and two) obtaining x′, now a two-dimensional sample vector consisting of two new features: