Machine Learning with R Cookbook - Second Edition

How it works...

SVD is a factorization of a real or complex matrix. In detail, the SVD of m x n matrix, A, is the factorization of A into the product of three matrices, . Here, U is an m x m orthonormal matrix, D has singular values and is an m x n diagonal matrix, and V^T is an n x n orthonormal matrix.

In this recipe, we demonstrate how to perform dimension reduction with SVD. First, you can apply the svd function on the swiss dataset to obtain factorized matrices. You can then generate two plots: one shows the variance explained in accordance to a singular vector, the other shows the cumulative variance explained in accordance to a singular ...