Linear Dimensionality Reduction
Linear Dimensionality Reduction
English | 2025 | ISBN: 3031957849 | 166 Pages | PDF EPUB (True) | 11 MB
English | 2025 | ISBN: 3031957849 | 166 Pages | PDF EPUB (True) | 11 MB
This book provides an overview of some classical linear methods in Multivariate Data Analysis. This is an old domain, well established since the 1960s, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with a geometric flavor. Both approaches are tightly linked: it is easier to learn patterns from data in low-dimensional spaces than in high-dimensional ones. It is shown how a diversity of methods and tools boil down to a single core method, PCA with SVD, so that the efforts to optimize codes for analyzing massive data sets like distributed memory and task-based programming, or to improve the efficiency of algorithms like Randomized SVD, can focus on this shared core method, and benefit all methods.