A PCA object that holds the data structures needed for mapping points to the PCA space.
A point transformer that will scale the points before being passed on to the PCA. For now, only a ZscoreTransformer is used.
The number of principal components to keep. Components are ranked according to the amount of variance they explain, so the most important dimensions are kept.
Transform a sequence of point to their z-score values, thereby scaling the dataset, and then transform them into principal components space, and finally, reduce their dimensionality by taking the top dimensions.
Transform a sequence of point to their z-score values, thereby scaling the dataset, and then transform them into principal components space, and finally, reduce their dimensionality by taking the top dimensions.
A transformer that scales a set of points, maps them into a PCA space, and then reduces the dimensionality by retaining only the top components.