Tutorial 2: Principal Component Analysis
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.
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.
Topics covered in this lesson
- How to calculate the eigenvectors of the sample covariance matrix
- How to perform PCA by projecting the data onto the eigenvectors
- PCA implementation
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