Essential Math for Data Science: Eigenvectors and Application to PCA - KDnuggets

Matrix decomposition, also called matrix factorization is the process of splitting a matrix into multiple pieces. In the context of data science, you can for instance use it to select parts of the data, aimed at reducing dimensionality without losing much information (as for instance in Principal Component Analysis, as you'll later in this post). Some operations are also more easily computed on the matrices resulting from the decomposition. In this article, you'll learn about the eigendecomposition of a matrix. One way to understand it is to consider it as a special change of basis (more details about change of basis in my last post).