If you know a bit of linear algebra the transformation is surprisingly intuitive. Your goal is to create a set of orthogonal vectors, each that captures the hi

If you know a bit of linear algebra the transformation is surprisingly intuitive. Your goal is to create a set of orthogonal vectors, each that captures the highest amount of variance in the original data (the assumption is that variance is where the most information is). This is achieved by performing Eigen-decomposition on the Covariance matrix of the original data. Essentially you are learning the eigenvectors of the covariance matrix, ordered by their eigenvalues.