In this chapter, we will cover the following recipes:
- Reducing dimensionality with PCA
- Using factor analysis for decomposition
- Using kernel PCA for nonlinear dimensionality reduction
- Using truncated SVD to reduce dimensionality
- Using decomposition to classify with DictionaryLearning
- Doing dimensionality reduction with manifolds – t-SNE
- Testing methods to reduce dimensionality with pipelines
