Curse Of Dimensionality Reveals High-Dimensional Pitfalls

This article explains the 'Curse of Dimensionality,' illustrating how high-dimensional spaces (e.g., 100 vs. 10 dimensions) become exponentially vast and sparse, undermining intuitive notions of distance and center. It details why nearest-neighbor and similarity measures degrade in high dimensions and highlights implications for datasets, neural networks, and feature engineering, urging practitioners to adopt dimensionality reduction and alternative metrics.