# Uses of Packagede.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter

• Packages that use de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter
Package Description
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.algorithm.clustering.gdbscan
Generalized DBSCAN Generalized DBSCAN is an abstraction of the original DBSCAN idea, that allows the use of arbitrary "neighborhood" and "core point" predicates.
de.lmu.ifi.dbs.elki.algorithm.outlier
Outlier detection algorithms
de.lmu.ifi.dbs.elki.datasource.filter.transform
Data space transformations
de.lmu.ifi.dbs.elki.index.preprocessed.localpca
Index using a preprocessed local PCA
de.lmu.ifi.dbs.elki.math.linearalgebra.pca
Principal Component Analysis (PCA) and Eigenvector processing
de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter
Filter eigenvectors based on their eigenvalues.
•
Class and Description
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 and Description
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 and Description
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 and Description
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 and Description
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 and Description
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 and Description
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 and Description
DropEigenPairFilter
The "drop" filter looks for the largest drop in normalized relative eigenvalues.
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.
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.
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".