Package de.lmu.ifi.dbs.elki.varianceanalysis

Classes for analysis of variance by different methods.

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.
PCA	A PCA is a principal component analysis that belongs to an object stored in a database.

Class Summary

AbstractPCA	Abstract super class for pca algorithms.
CompositeEigenPairFilter	The CompositeEigenPairFilter can be used to build a chain of eigenpair filters.
FilteredEigenPairs	Encapsulates weak and stromg 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.
GlobalPCA<O extends RealVector<O,?>>	Computes the principal components for vector objects of a given database.
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.
LinearLocalPCA<V extends RealVector<V,?>>	Performs a linear local PCA based on the covariance matrices of given objects.
LocalKernelPCA<V extends RealVector<V,?>>	Performs a local kernel PCA based on the kernel matrices of given objects.
LocalPCA<V extends RealVector<V,?>>	LocalPCA is a super calss for PCA-algorithms considering only a local neighborhood.
NormalizingEigenPairFilter	The NormalizingEigenPairFilter normalizes all eigenvectors s.t.
PercentageEigenPairFilter	The PercentageEigenPairFilter sorts the eigenpairs in decending 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.

Package de.lmu.ifi.dbs.elki.varianceanalysis Description

Classes for analysis of variance by different methods.