Chapter 5: Compressing Data via Dimensionality Reduction

Chapter Outline

• Unsupervised dimensionality reduction via principal component analysis

  • The main steps behind principal component analysis

  • Extracting the principal components step by step

  • Total and explained variance

  • Feature transformation

  • Principal component analysis in scikit-learn

• Supervised data compression via linear discriminant analysis

  • Principal component analysis versus linear discriminant analysis

  • The inner workings of linear discriminant analysis

  • Computing the scatter matrices

  • Selecting linear discriminants for the new feature subspace

  • Projecting samples onto the new feature space

  • LDA via scikit-learn

• Nonlinear dimensionality reduction techniques

  • Visualizing data via t-distributed stochastic neighbor embedding

• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.