most citedOn the Feasibility of Distinguishing Between Process Disturbances and Intrusions in Process Control Systems Using Multivariate Statistical Process Control

6 citations · 6 across the 1 of their papers we have counts for

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stat.ML2019

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

J. Camacho, A. K. Smilde, E. Saccenti +1

Sparse Principal Component Analysis (sPCA) is a popular matrix factorization approach based on Principal Component Analysis (PCA) that combines variance maximization and sparsity w…

stat.ML2019

Cross-product Penalized Component Analysis (XCAN)

José Camacho, Evrim Acar, Morten A. Rasmussen +1

Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (XCAN), a sparse matrix…