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

Class WeightedCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>

java.lang.Object

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.AbstractCovarianceMatrixBuilder<V>

de.lmu.ifi.dbs.elki.math.linearalgebra.pca.WeightedCovarianceMatrixBuilder<V>

Type Parameters:

V - Vector class to use

All Implemented Interfaces:

CovarianceMatrixBuilder<V>, InspectionUtilFrequentlyScanned, Parameterizable

@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="H.-P. Kriegel, P. Kr\u00f6ger, E. Schubert, A. Zimek",
           title="A General Framework for Increasing the Robustness of PCA-based Correlation Clustering Algorithms",
           booktitle="Proceedings of the 20th International Conference on Scientific and Statistical Database Management (SSDBM), Hong Kong, China, 2008",
           url="http://dx.doi.org/10.1007/978-3-540-69497-7_27")
public class WeightedCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>
extends AbstractCovarianceMatrixBuilder<V>

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 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 In: Proc. 20th Int. Conf. on Scientific and Statistical Database Management (SSDBM), 2008, Hong Kong Lecture Notes in Computer Science 5069, Springer

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static class	WeightedCovarianceMatrixBuilder.Parameterizer<V extends NumberVector<V,?>> Parameterization class.

Field Summary

Fields

Modifier and Type	Field and Description
static OptionID	WEIGHT_ID Parameter to specify the weight function to use in weighted PCA, must implement WeightFunction .
private PrimitiveDistanceFunction<? super V,DoubleDistance>	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

Methods

Modifier and Type	Method and Description
Matrix	processIds(DBIDs ids, Relation<? extends V> database) Weighted Covariance Matrix for a set of IDs.
<D extends NumberDistance<?,?>> Matrix	processQueryResults(Collection<? extends DistanceResultPair<D>> results, Relation<? extends V> database, int k) Compute Covariance Matrix for a QueryResult Collection By default it will just collect the ids and run processIds

Methods inherited from class de.lmu.ifi.dbs.elki.math.linearalgebra.pca.AbstractCovarianceMatrixBuilder

processDatabase, processQueryResults

Methods inherited from class java.lang.Object

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

Field Detail

WEIGHT_ID

public static final OptionID WEIGHT_ID

Parameter to specify the weight function to use in weighted PCA, must implement WeightFunction .

Key: -pca.weight

weightfunction

protected WeightFunction weightfunction

Holds the weight function.

weightDistance

private PrimitiveDistanceFunction<? super V extends NumberVector<? extends V,?>,DoubleDistance> weightDistance

Holds the distance function used for weight calculation

Constructor Detail

WeightedCovarianceMatrixBuilder

public WeightedCovarianceMatrixBuilder(WeightFunction weightfunction)

Constructor.

Parameters:

weightfunction -

Method Detail

processIds

public Matrix processIds(DBIDs ids,
                Relation<? extends V> database)

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<V extends NumberVector<? extends V,?>>

Specified by:

processIds in class AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>

Parameters:

ids - a collection of ids

database - the database used

Returns:

Covariance Matrix

processQueryResults

public <D extends NumberDistance<?,?>> Matrix processQueryResults(Collection<? extends DistanceResultPair<D>> results,
                                                         Relation<? extends V> 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<V extends NumberVector<? extends V,?>>

Overrides:

processQueryResults in class AbstractCovarianceMatrixBuilder<V extends NumberVector<? extends V,?>>

Parameters:

results - a collection of QueryResults

database - the database used

k - number of elements to process

Returns:

Covariance Matrix