Center for Imaging Science Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported Yes Johns Hopkins University NITRC Principal Components Analysis of Scalar, Vector, and Mesh Vertex Data An implementation of standard PCA algorithms for use on scalar or vector data sets. Kernel PCA is implemented in this class as well, where the data sets are scalar or vector valued functions assigned at each of the points in a PointSet. A Gaussian Distance Kernel class is provided with the PCA class. This contribution is part of a shape analysis software pipeline created at Johns Hopkins. PCA will be used to develop a set of basis vectors for use with Gaussian Random Field analysis. The output of PCA will be analyzed for significance with various statistical methods such as t-tests built upon the built-in statistical capabilities of ITK. Principal Components Analysis of Scalar, Vector, and Mesh Vertex Data Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported, Domain Independent http://www.nitrc.org/projects/pca-scalar-mesh/, http://www.nitrc.org/projects/pca-scalar-mesh/