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

Class SignificantEigenPairFilter

java.lang.Object

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter.SignificantEigenPairFilter

All Implemented Interfaces:

EigenPairFilter

@Title(value="Significant EigenPair Filter")
 @Description(value="Sorts the eigenpairs in decending order of their eigenvalues and looks for the maxmimum contrast of current Eigenvalue / average of remaining Eigenvalues.")
public class SignificantEigenPairFilter
extends java.lang.Object
implements EigenPairFilter

The SignificantEigenPairFilter sorts the eigenpairs in descending order of their eigenvalues and chooses the contrast of an Eigenvalue to the remaining Eigenvalues is maximal. It is closely related to the WeakEigenPairFilter and RelativeEigenPairFilter. But while the RelativeEigenPairFilter chooses the highest dimensionality that satisfies the relative alpha levels, the SignificantEigenPairFilter will chose the local dimensionality such that the 'contrast' is maximal. There are some situations where one or the other is superior, especially when it comes to handling nested clusters and strong global correlations that are not too interesting. These benefits usually only make a difference at higher dimensionalities.

Since:

0.2

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	SignificantEigenPairFilter.Parameterizer Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
static double	DEFAULT_WALPHA The default value for walpha.
private double	walpha The noise tolerance level for weak eigenvectors

Fields inherited from interface de.lmu.ifi.dbs.elki.math.linearalgebra.pca.filter.EigenPairFilter

PCA_EIGENPAIR_FILTER

Constructor Summary

Constructors

Constructor and Description
SignificantEigenPairFilter(double walpha) Constructor.

Method Summary

Modifier and Type	Method and Description
int	filter(double[] eigenPairs) Filters the specified eigenvalues into strong and weak eigenvalues, where strong eigenvalues have high variance and weak eigenvalues have small variance.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

DEFAULT_WALPHA

public static final double DEFAULT_WALPHA

The default value for walpha. Not used by default, we're going for maximum contrast only.

See Also:

Constant Field Values

walpha

private double walpha

The noise tolerance level for weak eigenvectors

Constructor Detail

SignificantEigenPairFilter

public SignificantEigenPairFilter(double walpha)

Constructor.

Parameters:

walpha -

Method Detail

filter

public int filter(double[] eigenPairs)

Description copied from interface: EigenPairFilter

Filters the specified eigenvalues into strong and weak eigenvalues, where strong eigenvalues have high variance and weak eigenvalues have small variance.

Specified by:

filter in interface EigenPairFilter

Parameters:

eigenPairs - the array of eigenvalues, must be sorted descending

Returns:

the number of eigenvectors to keep