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

Class WeightedCovarianceMatrixBuilder

java.lang.Object

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

All Implemented Interfaces:

CovarianceMatrixBuilder

@Title(value="Weighted Covariance Matrix / PCA")
 @Description(value="A PCA modification by using weights while building the covariance matrix, to obtain more stable results")
 @Reference(authors="Hans-Peter Kriegel, Peer Kr\u00f6ger, Erich Schubert, Arthur Zimek",
           title="A General Framework for Increasing the Robustness of PCA-based Correlation Clustering Algorithms",
           booktitle="Proc. 20th Intl. Conf. on Scientific and Statistical Database Management (SSDBM)",
           url="https://doi.org/10.1007/978-3-540-69497-7_27",
           bibkey="DBLP:conf/ssdbm/KriegelKSZ08")
public class WeightedCovarianceMatrixBuilder
extends java.lang.Object
implements CovarianceMatrixBuilder

CovarianceMatrixBuilder with weights.

This builder uses a weight function to weight points differently during build a covariance matrix. Covariance can be canonically extended with weights, as shown in the article

Reference:

A General Framework for Increasing the Robustness of PCA-Based Correlation Clustering Algorithms
Hans-Peter Kriegel and Peer Kröger and Erich Schubert and Arthur Zimek
Proc. 20th Int. Conf. on Scientific and Statistical Database Management (SSDBM)

Since:

0.2

Author:

Erich Schubert

Nested Class Summary

Nested Classes

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

Field Summary

Fields

Modifier and Type	Field and Description
private PrimitiveDistanceFunction<? super NumberVector>	weightDistance Holds the distance function used for weight calculation.
protected WeightFunction	weightfunction Holds the weight function.

Constructor Summary

Constructors

Constructor and Description
WeightedCovarianceMatrixBuilder(WeightFunction weightfunction) Constructor.

Method Summary

Modifier and Type	Method and Description
double[][]	processIds(DBIDs ids, Relation<? extends NumberVector> relation) Weighted Covariance Matrix for a set of IDs.
double[][]	processQueryResults(DoubleDBIDList results, Relation<? extends NumberVector> database, int k) Compute Covariance Matrix for a QueryResult Collection.

Methods inherited from class java.lang.Object

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

Methods inherited from interface de.lmu.ifi.dbs.elki.math.linearalgebra.pca.CovarianceMatrixBuilder

processQueryResults, processRelation

Field Detail

weightfunction

protected WeightFunction weightfunction

Holds the weight function.

weightDistance

private PrimitiveDistanceFunction<? super NumberVector> weightDistance

Holds the distance function used for weight calculation.

Constructor Detail

WeightedCovarianceMatrixBuilder

public WeightedCovarianceMatrixBuilder(WeightFunction weightfunction)

Constructor.

Parameters:

weightfunction - Weighting function

Method Detail

processIds

public double[][] processIds(DBIDs ids,
                             Relation<? extends NumberVector> relation)

Weighted Covariance Matrix for a set of IDs. Since we are not supplied any distance information, we'll need to compute it ourselves. Covariance is tied to Euclidean distance, so it probably does not make much sense to add support for other distance functions?

Specified by:

processIds in interface CovarianceMatrixBuilder

Parameters:

ids - Database ids to process

relation - Relation to process

Returns:

Covariance matrix

processQueryResults

public double[][] processQueryResults(DoubleDBIDList results,
                                      Relation<? extends NumberVector> database,
                                      int k)

Compute Covariance Matrix for a QueryResult Collection. By default it will just collect the ids and run processIds

Specified by:

processQueryResults in interface CovarianceMatrixBuilder

Parameters:

results - a collection of QueryResults

database - the database used

k - number of elements to process

Returns:

Covariance Matrix