cuml.decomposition

PCA	PCA (Principal Component Analysis) is a fundamental dimensionality reduction technique used to combine features in X in linear combinations such that each new component captures the most information or variance of the data.
IncrementalPCA	Based on sklearn.decomposition.IncrementalPCA from scikit-learn 0.23.1
TruncatedSVD	TruncatedSVD is used to compute the top K singular values and vectors of a large matrix X.