Data-Driven Risk Factors and Asset Allocation with Unsupervised Learning
Chapter 6, The Machine Learning Process, introduced how unsupervised learning adds value by uncovering structures in data without the need for an outcome variable to guide the search process. This contrasts with supervised learning, which was the focus of the last several chapters: instead of predicting future outcomes, unsupervised learning aims to learn an informative representation of the data that helps explore new data, discover useful insights, or solve some other task more effectively.
Dimensionality reduction and clustering are the main tasks for unsupervised learning:
- Dimensionality reduction transforms the existing features into a new, smaller set while minimizing the loss of information. Algorithms differ by how they measure the loss of information, whether they apply linear or nonlinear transformations or which constraints they impose on the new feature set.
- Clustering algorithms...
