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

Principal Component Analysis (PCA) and Eigenvector processing.

See:
          Description


Interface Summary
CovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> 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
AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>> 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.
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.
PCAFilteredResult Result class for a filtered PCA.
PCAFilteredRunner<V extends NumberVector<? extends V,?>> PCA runner that will do dimensionality reduction.
PCAFilteredRunner.Parameterizer<V extends NumberVector<? extends V,?>> Parameterization class.
PCAResult Result class for Principal Component Analysis with some convenience methods
PCARunner<V extends NumberVector<? extends V,?>> Class to run PCA on given data.
PCARunner.Parameterizer<V extends NumberVector<? extends V,?>> 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.
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<V extends NumberVector<? extends V,?>> 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<V extends NumberVector<? extends V,?>> CovarianceMatrixBuilder with weights.
WeightedCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<V,?>> Parameterization class.
 

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

Principal Component Analysis (PCA) and Eigenvector processing.


Release 0.4.0 (2011-09-20_1324)