Chapter 13 Singular Value Decomposition

One of the most important decompositions in matrix algebra is known as the Singular Value Decomposition, commonly known as SVD. One of the reasons why this decomposition is such fundamental is because it can be applied to any type of matrix—rectangular or square, singular or nonsigular. In this chapter, you will learn about SVD and its role for statistical learning methods.