- 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.