Principal Component Analysis

We describe principal component analysis (PCA), a popular technique to process multidimensional data. We explain that PCA identifies the directions of maximum variance of a random vector via the eigendecomposition of its covariance matrix. We then show that applying PCA to the sample covariance matrix of a dataset reveals the components with maximum sample variance.

Photo by Steve Smith on Unsplash