Johnson-Lindenstrauss Theorem

This lecture covers the Johnson-Lindenstrauss Theorem, which states that a set of points in high-dimensional space can be embedded into a lower-dimensional space while preserving the pairwise distances between the points. The theorem is based on the concept of concentration of measure and has important applications in machine learning, data analysis, and dimensionality reduction.