TY  - GEN 
AU  - Camacho Páez, José
AU  - Smilde, A. K.
AU  - Saccenti, E.
AU  - Westerhuis, J. A.
AU  - Bro, R.
PY  - 2025
UR  - https://hdl.handle.net/10481/106469
AB  - Sparse Principal Component Analysis (sPCA) is a popular matrix factorization that combines variance
maximization and sparsity with the ultimate goal of improving data interpretation. In this series of papers we
show that the factorization with sPCA...
LA  - eng
PB  - Elsevier
TI  - All sparse PCA models are wrong, but some are useful. Part III: Model interpretation
DO  - 10.1016/j.chemolab.2025.105498
ER  -