Principal Component Analysis (PCA): Theory, Mathematics, and Applications (opens in new tab)

Author(s): Praveen Bhavani Originally published on Towards AI. Principal Component Analysis (PCA) is one of the most widely used techniques for dimensionality reduction and feature extraction. PCA transforms correlated variables into a smaller set of uncorrelated variables called principal components, while preserving as much information (variance) as possible. PCA is fundamentally a linear algebra and statistical method rooted in: Covariance structure analysis Orthogonal transformations Eige...