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Tutorial 3: Dimensionality reduction and reconstruction

Difficulty level

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.

Topics covered in this lesson
  • How to perform PCA on MNIST dataset
  • How to calculate variance
  • How to reconstruct data with different PCs
  • Visualization of the weights
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