Sebastian Raschka, 2015
Python Machine Learning - Code Examples
- Unsupervised dimensionality reduction via principal component analysis 128
- Total and explained variance
- Feature transformation
- Principal component analysis in scikit-learn
- Supervised data compression via 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
- Using kernel principal component analysis for nonlinear mappings
- Kernel functions and the kernel trick
- Implementing a kernel principal component analysis in Python
- Example 1 – separating half-moon shapes
- Example 2 – separating concentric circles
- Projecting new data points
- Kernel principal component analysis in scikit-learn
- Summary