Hands-On Machine Learning for Algorithmic Trading

Linear dimensionality reduction

Linear dimensionality reduction algorithms compute linear combinations that translate, rotate, and rescale the original features to capture significant variation in the data, subject to constraints on the characteristics of the new features.

Principal Component Analysis (PCA), invented in 1901 by Karl Pearson, finds new features that reflect directions of maximal variance in the data while being mutually uncorrelated, or orthogonal.

Independent Component Analysis (ICA), in contrast, originated in signal processing in the 1980s, with the goal of separating different signals while imposing the stronger constraint of statistical independence.

This section introduces these two algorithms and then illustrates ...