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

Packages that use de.lmu.ifi.dbs.elki.math.linearalgebra.pca

de.lmu.ifi.dbs.elki.algorithm	Algorithms suitable as a task for the KDDTask main routine.
de.lmu.ifi.dbs.elki.algorithm.clustering.correlation	Correlation clustering algorithms
de.lmu.ifi.dbs.elki.data.model	Cluster models classes for various algorithms.
de.lmu.ifi.dbs.elki.distance.distancefunction.correlation	Distance functions using correlations.
de.lmu.ifi.dbs.elki.distance.distancefunction.subspace	Distance functions based on subspaces.
de.lmu.ifi.dbs.elki.index.preprocessed.localpca	Index using a preprocessed local PCA.
de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj	Index using a preprocessed local subspaces.
de.lmu.ifi.dbs.elki.math.linearalgebra.pca	Principal Component Analysis (PCA) and Eigenvector processing.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.algorithm

PCAFilteredRunner
PCA runner that will do dimensionality reduction.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.algorithm.clustering.correlation

PCARunner
Class to run PCA on given data.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.data.model

PCAFilteredResult
Result class for a filtered PCA.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.distance.distancefunction.correlation

PCAFilteredResult
Result class for a filtered PCA.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.distance.distancefunction.subspace

PCAFilteredResult
Result class for a filtered PCA.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.index.preprocessed.localpca

PCAFilteredResult
Result class for a filtered PCA.

PCAFilteredRunner
PCA runner that will do dimensionality reduction.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.index.preprocessed.subspaceproj

PCAFilteredResult
Result class for a filtered PCA.

PCAFilteredRunner
PCA runner that will do dimensionality reduction.

Classes in de.lmu.ifi.dbs.elki.math.linearalgebra.pca used by de.lmu.ifi.dbs.elki.math.linearalgebra.pca

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.

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.

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.

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.

PCAFilteredResult
Result class for a filtered PCA.

PCAFilteredRunner
PCA runner that will do dimensionality reduction.

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.

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.

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
CovarianceMatrixBuilder with weights.