Tutorial 3: Dimensionality reduction and reconstruction
This tutorial covers how to apply principal component analysis (PCA) for dimensionality reduction, using a classic dataset that is often used to benchmark machine learning algorithms: MNIST. We'll also learn how to use PCA for reconstruction and denoising.
Overview of this tutorial:
- Perform PCA on MNIST
- Calculate the variance explained
- Reconstruct data with different numbers of PCs
- (Bonus) Examine denoising using PCA
You can learn more about MNIST dataset here.
- How to perform PCA on MNIST dataset
- How to calculate variance
- How to reconstruct data with different PCs
- Visualization of the weights