The source describes dimensionality reduction, a technique used to simplify and improve the performance of machine learning algorithms when dealing with high-dimensional datasets. The curse of dimensionality refers to the challenges that arise when analyzing data with many features, such as difficulties in optimization and the loss of contrast between data points. Subspace models are introduced as a way to address this by identifying lower-dimensional subspaces where the data may reside. Dimensionality reduction techniques include feature selection, which chooses a subset of the original features, and feature extraction, which computes new features from the original ones. Examples of feature extraction methods include Principal Component Analysis (PCA), which finds the directions of greatest variation in the data, and Multi-Dimensional Scaling (MDS), which aims to minimize the "stress" associated with embedding data points in a lower-dimensional space.