June 2025 · Dimensionality Reduction · Data Science
Principal Component Analysis (PCA) is a powerful unsupervised technique used to reduce the dimensionality of large datasets. It identifies directions (called principal components) that maximize the variance in the data, and projects it along those new axes.
In practical terms, PCA helps compress and visualize high-dimensional datasets while retaining the most important patterns. I've used PCA for anomaly detection, preprocessing for clustering, and improving model interpretability.
Mathematically, PCA relies on eigenvalues and eigenvectors of the covariance matrix, and selects components with the highest variance contribution. It’s a fundamental tool for any data scientist.