Lecture 23 – Principal Component Analysis
by Josh Hug (Fall 2019)
Make sure to complete the Quick Check questions in between each video. These are ungraded, but it’s in your best interest to do them.
Definition and computation of principal components. Geometric interpretation of principal components and low rank approximations. Data centering.
Review of regression. Comparing the SVD and regression. Minimizing the perpendicular error.
Interpretation of singular values. The relationship between singular values and variance. Analyzing scree plots.
Introduction to principal Component analysis (PCA). PCA for exploratory data analysis.