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Table 22 Method for dimension reduction: Supervised principal components

From: Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

Supervised principal components (SuperPC)

 PCA is conducted based on a subset of preliminarily selected variables. In SuperPC [55], first a variable selection method (see above) is used to reduce the number of prediction variables. This means that the additional step in comparison with PCA is that the subset of predictors selected is based on their association with an outcome, explaining the name supervised. Then, a classical PCA is performed on the reduced space (i.e., only considering the selected variables). The newly constructed components are then used for prediction