| Package | Description |
|---|---|
| gov.sandia.cognition.learning.algorithm.pca |
Provides implementations of Principle Components Analysis (PCA).
|
| Class and Description |
|---|
| 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.
|