E nvironment for Deve L oping K DD-Applications Supported by I ndex-Structures

## Package de.lmu.ifi.dbs.elki.math.linearalgebra.pca

Principal Component Analysis (PCA) and Eigenvector processing.

See:
Description

Interface Summary
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 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.

## Package de.lmu.ifi.dbs.elki.math.linearalgebra.pca Description

Principal Component Analysis (PCA) and Eigenvector processing.

 Release 0.3 (2010-03-31_1612)