See: Description
| Interface | Description |
|---|---|
| CovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> |
Interface for computing covariance matrixes on a data set.
|
| EigenPairFilter |
The eigenpair filter is used to filter eigenpairs (i.e. eigenvectors
and their corresponding eigenvalues) which are a result of a
Variance Analysis Algorithm, e.g.
|
| Class | Description |
|---|---|
| AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> |
Abstract class with the task of computing a Covariance matrix to be used in PCA.
|
| CompositeEigenPairFilter |
The
CompositeEigenPairFilter can be used to build a chain of
eigenpair filters. |
| CompositeEigenPairFilter.Parameterizer |
Parameterization class.
|
| 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.
|
| FirstNEigenPairFilter.Parameterizer |
Parameterization class.
|
| 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.
|
| LimitEigenPairFilter.Parameterizer |
Parameterization class.
|
| NormalizingEigenPairFilter |
The NormalizingEigenPairFilter normalizes all eigenvectors s.t.
|
| PCAFilteredResult |
Result class for a filtered PCA.
|
| PCAFilteredRunner<V extends NumberVector<? extends V,?>> |
PCA runner that will do dimensionality reduction.
|
| PCAFilteredRunner.Parameterizer<V extends NumberVector<? extends V,?>> |
Parameterization class.
|
| PCAResult |
Result class for Principal Component Analysis with some convenience methods
|
| PCARunner<V extends NumberVector<? extends V,?>> |
Class to run PCA on given data.
|
| PCARunner.Parameterizer<V extends NumberVector<? extends V,?>> |
Parameterization class.
|
| 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.
|
| PercentageEigenPairFilter.Parameterizer |
Parameterization class.
|
| 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.
|
| ProgressiveEigenPairFilter.Parameterizer |
Parameterization class.
|
| 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.
|
| RelativeEigenPairFilter.Parameterizer |
Parameterization class.
|
| 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.
|
| SignificantEigenPairFilter.Parameterizer |
Parameterization class.
|
| StandardCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> |
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".
|
| WeakEigenPairFilter.Parameterizer |
Parameterization class.
|
| WeightedCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> |
CovarianceMatrixBuilder with weights. |
| WeightedCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<V,?>> |
Parameterization class.
|
Principal Component Analysis (PCA) and Eigenvector processing.