Package | Description |
---|---|
de.lmu.ifi.dbs.elki.math.linearalgebra.pca |
Principal Component Analysis (PCA) and Eigenvector processing.
|
Modifier and Type | Method and Description |
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FilteredEigenPairs |
WeakEigenPairFilter.filter(SortedEigenPairs eigenPairs)
Filter eigenpairs
|
FilteredEigenPairs |
SignificantEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
RelativeEigenPairFilter.filter(SortedEigenPairs eigenPairs)
Filter eigenpairs
|
FilteredEigenPairs |
ProgressiveEigenPairFilter.filter(SortedEigenPairs eigenPairs)
Filter eigenpairs.
|
FilteredEigenPairs |
PercentageEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
NormalizingEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
LimitEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
FirstNEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
EigenPairFilter.filter(SortedEigenPairs eigenPairs)
Filters the specified eigenpairs into strong and weak eigenpairs,
where strong eigenpairs having high variances
and weak eigenpairs having small variances.
|
FilteredEigenPairs |
DropEigenPairFilter.filter(SortedEigenPairs eigenPairs) |
FilteredEigenPairs |
CompositeEigenPairFilter.filter(SortedEigenPairs eigenPairs)
Filters the specified eigenpairs into strong and weak eigenpairs, where
strong eigenpairs having high variances and weak eigenpairs having small
variances.
|
Modifier and Type | Method and Description |
---|---|
private double |
PCAFilteredAutotuningRunner.computeExplainedVariance(FilteredEigenPairs filteredEigenPairs)
Compute the explained variance for a FilteredEigenPairs.
|
Constructor and Description |
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PCAFilteredResult(SortedEigenPairs eigenPairs,
FilteredEigenPairs filteredEigenPairs,
double big,
double small)
Construct a result object for the filtered PCA result.
|
Copyright © 2015 ELKI Development Team, Lehr- und Forschungseinheit für Datenbanksysteme, Ludwig-Maximilians-Universität München. License information.