NumPy: Linear Algebra on Images

In this instructional exercise, we will use a matrix decomposition from linear algebra, the Singular Value Decomposition, to generate a compressed approximation of an image. Let's start with What is Singular Value Decomposition? Singular Value Decomposition, or SVD, has an extensive variety of applications. These include dimensionality reduction, compressing images, and denoising data. Fundamentally, SVD says that a matrix can be represented as the product of three other matrices.