Uses of Package
gov.sandia.cognition.learning.algorithm.pca

Packages that use gov.sandia.cognition.learning.algorithm.pca
gov.sandia.cognition.learning.algorithm.pca Provides implementations of Principle Components Analysis (PCA). 
 

Classes in gov.sandia.cognition.learning.algorithm.pca used by gov.sandia.cognition.learning.algorithm.pca
AbstractPrincipalComponentsAnalysis
          Abstract implementation of PCA.
GeneralizedHebbianAlgorithm
          Implementation of the Generalized Hebbian Algorithm, also known as Sanger's Rule, which is a generalization of Oja's Rule.
KernelPrincipalComponentsAnalysis.Function
          The resulting transformation function learned by Kernel Principal Components Analysis.
PrincipalComponentsAnalysis
          Principal Components Analysis is a family of algorithms that map from a high-dimensional input space to a low-dimensional output space.
PrincipalComponentsAnalysisFunction
          This VectorFunction maps a high-dimension input space onto a (hopefully) simple low-dimensional output space by subtracting the mean of the input data, and passing the zero-mean input through a dimension-reducing matrix multiplication function.