# cuml.decomposition

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| [`PCA`](../generated/cuml.decomposition.PCA/index.html.md#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.   |
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| [`IncrementalPCA`](../generated/cuml.decomposition.IncrementalPCA/index.html.md#cuml.decomposition.IncrementalPCA) | Based on sklearn.decomposition.IncrementalPCA from scikit-learn 0.23.1                                                                                                                                                            |
| [`TruncatedSVD`](../generated/cuml.decomposition.TruncatedSVD/index.html.md#cuml.decomposition.TruncatedSVD)     | TruncatedSVD is used to compute the top K singular values and vectors of a large matrix X.                                                                                                                                        |