Dimensionality Reduction via Regression (DRR) is a manifold learning technique aimed at removing residual statistical dependence between PCA components due to dataset curvature. DRR predicts PCA coefficients from neighboring coefficients using multivariate regression, generalizing PPA. It advances dimensionality reduction methods by using curves instead of straight lines.