TY - JOUR

T1 - Cross-product penalized component analysis (X-CAN)

AU - Camacho, Jose

AU - Acar, Evrim

AU - Rasmussen, Morten A.

AU - Bro, Rasmus

PY - 2020

Y1 - 2020

N2 - Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (X-CAN), a matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation, with a focus on highlighting differences between groups of observations and/or variables. The approach is based on previous developments, notably (i) the Sparse Principal Component Analysis (SPCA) framework based on the LASSO, (ii) extensions of SPCA to constrain both modes of the factorization, like co-clustering or the Penalized Matrix Decomposition (PMD), and (iii) the Group-wise Principal Component Analysis (GPCA) method. The result is a flexible modeling approach that can be used for data exploration in a large variety of problems. We demonstrate its use with applications from different disciplines.

AB - Matrix factorization methods are extensively employed to understand complex data. In this paper, we introduce the cross-product penalized component analysis (X-CAN), a matrix factorization based on the optimization of a loss function that allows a trade-off between variance maximization and structural preservation, with a focus on highlighting differences between groups of observations and/or variables. The approach is based on previous developments, notably (i) the Sparse Principal Component Analysis (SPCA) framework based on the LASSO, (ii) extensions of SPCA to constrain both modes of the factorization, like co-clustering or the Penalized Matrix Decomposition (PMD), and (iii) the Group-wise Principal Component Analysis (GPCA) method. The result is a flexible modeling approach that can be used for data exploration in a large variety of problems. We demonstrate its use with applications from different disciplines.

KW - Data interpretation

KW - Group-wise principal component analysis

KW - Principal component analysis

KW - Sparse principal component analysis

KW - Sparsity

U2 - 10.1016/j.chemolab.2020.104038

DO - 10.1016/j.chemolab.2020.104038

M3 - Journal article

AN - SCOPUS:85087277428

VL - 203

JO - Chemometrics and Intelligent Laboratory Systems

JF - Chemometrics and Intelligent Laboratory Systems

SN - 0169-7439

M1 - 104038

ER -