vtkPCAStatistics

Section: Visualization Toolkit Infovis Classes

Usage

This class derives from the multi-correlative statistics algorithm and uses the covariance matrix and Cholesky decomposition computed by it. However, when it finalizes the statistics in Learn mode, the PCA class computes the SVD of the covariance matrix in order to obtain its eigenvectors.

In the assess mode, the input data are - projected into the basis defined by the eigenvectors, - the energy associated with each datum is computed, - or some combination thereof. Additionally, the user may specify some threshold energy or eigenvector entry below which the basis is truncated. This allows projection into a lower-dimensional state while minimizing (in a least squares sense) the projection error.

.SECTION Thanks Thanks to David Thompson, Philippe Pebay and Jackson Mayo from Sandia National Laboratories for implementing this class.

To create an instance of class vtkPCAStatistics, simply invoke its constructor as follows

  obj = vtkPCAStatistics

Methods

The class vtkPCAStatistics has several methods that can be used. They are listed below. Note that the documentation is translated automatically from the VTK sources, and may not be completely intelligible. When in doubt, consult the VTK website. In the methods listed below, obj is an instance of the vtkPCAStatistics class.