Principal Polynomial Analysis (PPA) is a manifold learning technique that generalizes PCA by using principal polynomials to capture nonlinear data patterns. It improves PCA’s energy compaction ability, reducing dimensionality reduction errors. PPA defines a manifold-dependent metric that generalizes Mahalanobis distance for curved manifolds.