Principal Component Variance Maximization: How Eigenvalue Decomposition Enables Linear Dimensionality ReductionPrincipal Component Variance Maximization: How Eigenvalue Decomposition Enables Linear Dimensionality Reduction
When you work with real datasets—customer behaviour logs, sensor readings, marketing metrics, or model features—you often face a familiar problem: too many variables, too much redundancy, and slower modelling with