Unsupervised learning algorithms

There are two tasks that we are mostly concerned with in unsupervised learning: dimensionality reduction and clustering.

Dimensionality reduction

Dimensionality reduction is used to help to visualize higher-dimensional data systematically. This is useful because the human brain can visualize only three spatial dimensions (and possibly, a temporal one), but most datasets involve much higher dimensions.

The typical technique used in dimensionality reduction is Principal Component Analysis (PCA). PCA involves using linear algebra techniques to project higher-dimensional data onto a lower-dimensional space. This inevitably involves the loss of information, but often, by projecting along the correct...