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

Class PCAFilteredAutotuningRunner

java.lang.Object

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

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

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

@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="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 PCAFilteredAutotuningRunner
extends PCAFilteredRunner

Performs a self-tuning local PCA based on the covariance matrices of given objects. At most the closest 'k' points are used in the calculation and a weight function is applied. The number of points actually used depends on when the strong eigenvectors exhibit the clearest correlation.

Since:

0.5.0

Author:

Erich Schubert

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
(package private) static class	PCAFilteredAutotuningRunner.Cand Candidate
static class	PCAFilteredAutotuningRunner.Parameterizer Parameterization class.

Field Summary

Fields inherited from class de.lmu.ifi.dbs.elki.math.linearalgebra.pca.PCARunner

covarianceMatrixBuilder

Constructor Summary

Constructors

Constructor and Description
PCAFilteredAutotuningRunner(CovarianceMatrixBuilder covarianceMatrixBuilder, EigenPairFilter eigenPairFilter, double big, double small) Constructor.

Method Summary

Methods

Modifier and Type	Method and Description
private void	assertSortedByDistance(DoubleDBIDList results) Ensure that the results are sorted by distance.
private double	computeExplainedVariance(FilteredEigenPairs filteredEigenPairs) Compute the explained variance for a FilteredEigenPairs.
PCAFilteredResult	processIds(DBIDs ids, Relation<? extends NumberVector> database) Run PCA on a collection of database IDs.
PCAFilteredResult	processQueryResult(DoubleDBIDList results, Relation<? extends NumberVector> database) Run PCA on a QueryResult Collection.

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

getEigenPairFilter, processCovarMatrix, processEVD

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

getCovarianceMatrixBuilder, processDatabase, setCovarianceMatrixBuilder

Methods inherited from class java.lang.Object

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

Constructor Detail

PCAFilteredAutotuningRunner

public PCAFilteredAutotuningRunner(CovarianceMatrixBuilder covarianceMatrixBuilder,
                           EigenPairFilter eigenPairFilter,
                           double big,
                           double small)

Constructor.

Parameters:

covarianceMatrixBuilder - Covariance matrix builder

eigenPairFilter - Eigen pair filter

big - Replacement for large values

small - Replacement for small values

Method Detail

processIds

public PCAFilteredResult processIds(DBIDs ids,
                           Relation<? extends NumberVector> database)

Description copied from class: PCAFilteredRunner

Run PCA on a collection of database IDs.

Overrides:

processIds in class PCAFilteredRunner

Parameters:

ids - a collection of ids

database - the database used

Returns:

PCA result

processQueryResult

public PCAFilteredResult processQueryResult(DoubleDBIDList results,
                                   Relation<? extends NumberVector> database)

Description copied from class: PCAFilteredRunner

Run PCA on a QueryResult Collection.

Overrides:

processQueryResult in class PCAFilteredRunner

Parameters:

results - a collection of QueryResults

database - the database used

Returns:

PCA result

computeExplainedVariance

private double computeExplainedVariance(FilteredEigenPairs filteredEigenPairs)

Compute the explained variance for a FilteredEigenPairs.

Parameters:

filteredEigenPairs - Filtered eigenpairs

Returns:

explained variance by the strong eigenvectors.

assertSortedByDistance

private void assertSortedByDistance(DoubleDBIDList results)

Ensure that the results are sorted by distance.

Parameters:

results - Results to process