Principal Component Analysis (PCA) is a widely used statistical technique in the field of data analysis and dimensionality reduction. It’s particularly effective for reducing the dimensionality of large datasets while preserving the essential information. PCA accomplishes this by transforming the original variables into a new set of variables, called principal components, which are linear combinations of the original variables. Lanczos method can also be used to perform PCA in a computationally efficient manner.