Class Summary |
CompositeEigenPairFilter |
The CompositeEigenPairFilter can be used to build a chain of
eigenpair filters. |
CovarianceMatrixBuilder<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
Abstract class with the task of computing a Covariance matrix to be used in PCA. |
FilteredEigenPairs |
Encapsulates weak and strong eigenpairs that have been filtered out
by an eigenpair filter. |
FirstNEigenPairFilter |
The FirstNEigenPairFilter marks the n highest eigenpairs as strong
eigenpairs, where n is a user specified number. |
KernelCovarianceMatrixBuilder<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
Kernel Covariance Matrix Builder. |
LimitEigenPairFilter |
The LimitEigenPairFilter marks all eigenpairs having an (absolute) eigenvalue
below the specified threshold (relative or absolute) as weak eigenpairs, the
others are marked as strong eigenpairs. |
NormalizingEigenPairFilter |
The NormalizingEigenPairFilter normalizes all eigenvectors s.t. |
PCAFilteredResult |
Result class for a filtered PCA. |
PCAFilteredRunner<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
PCA runner that will do dimensionality reduction. |
PCAResult |
Result class for Principal Component Analysis with some convenience methods |
PCARunner<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
Class to run PCA on given data. |
PercentageEigenPairFilter |
The PercentageEigenPairFilter sorts the eigenpairs in descending order of
their eigenvalues and marks the first eigenpairs, whose sum of eigenvalues is
higher than the given percentage of the sum of all eigenvalues as strong
eigenpairs. |
ProgressiveEigenPairFilter |
The ProgressiveEigenPairFilter sorts the eigenpairs in descending order of
their eigenvalues and marks the first eigenpairs, whose sum of eigenvalues is
higher than the given percentage of the sum of all eigenvalues as strong
eigenpairs. |
RelativeEigenPairFilter |
The RelativeEigenPairFilter sorts the eigenpairs in descending order of their
eigenvalues and marks the first eigenpairs who are a certain factor above the
average of the remaining eigenvalues. |
SignificantEigenPairFilter |
The SignificantEigenPairFilter sorts the eigenpairs in descending order of
their eigenvalues and chooses the contrast of an Eigenvalue to the remaining
Eigenvalues is maximal. |
StandardCovarianceMatrixBuilder<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
Class for building a "traditional" covariance matrix. |
WeakEigenPairFilter |
The WeakEigenPairFilter sorts the eigenpairs in descending order of their
eigenvalues and returns the first eigenpairs who are above the average mark
as "strong", the others as "weak". |
WeightedCovarianceMatrixBuilder<V extends NumberVector<V,?>,D extends NumberDistance<D,?>> |
CovarianceMatrixBuilder with weights. |
Principal Component Analysis (PCA) and Eigenvector processing.