Explore the mathematical foundations of Principal Component Analysis (PCA) in this 1-hour 20-minute lecture from UofU Data Science. Learn about projections and basis vectors before diving into Singular Value Decomposition (SVD) and how PCA combines SVD with centering. The lecture also covers eigendecompositions, providing essential knowledge for dimensionality reduction techniques in data mining and analysis.
Data Mining Lecture 17 - Principal Component Analysis
Overview
Syllabus
Data Mining Lecture 17 - PCA
Taught by
UofU Data Science
