Tutorial 2: Principal component analysis
This tutorial covers how to perform principal component analysis (PCA) by projecting the data onto the eigenvectors of its covariance matrix.
Overview of this tutorial:
- Calculate the eigenvectors of the sample covariance matrix.
- Perform PCA by projecting data onto the eigenvectors of the covariance matrix.
- Plot and explore the eigenvalues.
To quickly refresh your knowledge of eigenvalues and eigenvectors, you can watch this short video (4 minutes) for a geometrical explanation. For a deeper understanding, this in-depth video (17 minutes) provides an excellent basis and is beautifully illustrated.
- How to calculate the eigenvectors of the sample covariance matrix
- How to perform PCA by projecting the data onto the eigenvectors
- PCA implementation