Random Projection

Random projection is a surprisingly powerful yet simple approach to dimensionality reduction. Based on the Johnson-Lindenstrauss idea, it shows that pairwise distances can be approximately preserved under random projections. This makes it especially important for very large, high-dimensional datasets where computational efficiency matters. Although interpretability is limited, its speed and scalability make it highly practical.