This is the note I used as an example of applications in Linear Algebra I lectured at Purdue University. It is slightly modified so that it is more or less self contained.
Principal Component Analysis (PCA) is a linear algebra technique for data analysis, which is an application of eigenvalues and eigenvectors. PCA can be used in
- exploratory data analysis (visualizing the data)
- features reduction
We will learn the basic idea of PCA and see its applications in handwritten-digits recognition, eigenfaces and etc.