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

Principal Component Analysis (PCA) and Eigenvector processing.

See: Description

Interface Summary

Interface	Description
CovarianceMatrixBuilder	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 Summary

Class	Description
AbstractCovarianceMatrixBuilder	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.
DropEigenPairFilter	The "drop" filter looks for the largest drop in normalized relative eigenvalues.
DropEigenPairFilter.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.
PCAFilteredAutotuningRunner	Performs a self-tuning local PCA based on the covariance matrices of given objects.
PCAFilteredAutotuningRunner.Cand	Candidate
PCAFilteredAutotuningRunner.Parameterizer	Parameterization class.
PCAFilteredResult	Result class for a filtered PCA.
PCAFilteredRunner	PCA runner that will do dimensionality reduction.
PCAFilteredRunner.Parameterizer	Parameterization class.
PCAResult	Result class for Principal Component Analysis with some convenience methods
PCARunner	Class to run PCA on given data.
PCARunner.Parameterizer	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.
RANSACCovarianceMatrixBuilder	RANSAC based approach to a more robust covariance matrix computation.
RANSACCovarianceMatrixBuilder.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	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	CovarianceMatrixBuilder with weights.
WeightedCovarianceMatrixBuilder.Parameterizer	Parameterization class.

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

Principal Component Analysis (PCA) and Eigenvector processing.