PCA scoring

Learn about configuring Singular Value Decomposition (SVD) to perform Principal Component Analysis (PCA) projections.

SVD models can be configured to perform PCA projections. PCA is closely related to SVD. PCA computes a set of orthonormal bases (principal components) that are ranked by their corresponding explained variance. The main difference between SVD and PCA is that the PCA projection is not scaled by the singular values. The PCA projection to the new coordinate system is given by:

Figure 7-14 PCA Projection Calculation

Description of "Figure 7-14 PCA Projection Calculation"

where (nxk) is the projected data in the reduced data space, defined by the first k components, and V_k defines the reduced component set.