The P position attribute is used. This is batched into consecutive groups; so all the input samples should have the same number of points and be concatenated together.

Each volume becomes a sample. All the volumes must match in resolution and tuple-size.

Find the principal components of the input data. The output will consist of the average along with the specified number of orthonormal components. The eigenvalue associated with each component is also provided in the eval attribute - this gives an indication of the importance of it. The component attribute stores which component it belongs to, with 0 being the average.

A Scree Chart provides a way to intuitively see where additional components will form diminishing returns. Two graphs are given; one is the relative contribution of each component; the other is the cumulative total of all components up to that point. Usually a bend in the graph is used as a signal of where additional components are noise. But one can also use the cumulative total to make sure a certain percentage of the variation is captured.

This projects the input sample onto the set of components specified by the second input. The second input is usually the result of another Principal Component Analysis SOP in Analyse mode.

The sample is projected onto each of the components generating an output point with a weight attribute specifying how much that component contributed. Linearly combining all the components with these weights will give the best reconstruction possible of the input sample.

Given a set of points with weight attributes; the second input’s components are linearly combined to form the output. This can be used to reconstruct a known sample. If it was projected with fewer components than the full dimensionality, this will remove the least-important variations in the original sample set, thereby filtering out uncorrelated noise.

The weights can also come from other sources, or be scaled to achieve interesting effects.