All Sparse PCA Models Are Wrong, But Some Are Useful. Part I: Computation of Scores, Residuals and Explained Variance

no code implementations • 9 Jul 2019 • J. Camacho, A. K. Smilde, E. Saccenti, J. A. Westerhuis

Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity with the ultimate goal of improving data interpretation.