org.openimaj.math.matrix.algorithm.pca

Class ThinSvdPrincipalComponentAnalysis

java.lang.Object

org.openimaj.math.matrix.algorithm.pca.PrincipalComponentAnalysis

org.openimaj.math.matrix.algorithm.pca.ThinSvdPrincipalComponentAnalysis

public class ThinSvdPrincipalComponentAnalysis
extends PrincipalComponentAnalysis

Compute the PCA using a thin SVD to extract the best-n principal components directly.

Author:

Jonathon Hare (jsh2@ecs.soton.ac.uk)

Nested Class Summary

Nested classes/interfaces inherited from class org.openimaj.math.matrix.algorithm.pca.PrincipalComponentAnalysis

PrincipalComponentAnalysis.ComponentSelector, PrincipalComponentAnalysis.EnergyThresholdComponentSelector, PrincipalComponentAnalysis.NumberComponentSelector, PrincipalComponentAnalysis.PercentageEnergyComponentSelector

Field Summary

Fields inherited from class org.openimaj.math.matrix.algorithm.pca.PrincipalComponentAnalysis

basis, eigenvalues, mean

Constructor Summary

Constructors

Constructor and Description
ThinSvdPrincipalComponentAnalysis(int ndims) Construct a ThinSvdPrincipalComponentAnalysis that will extract the n best eigenvectors.

Method Summary

Modifier and Type	Method and Description
void	learnBasisNorm(Jama.Matrix data) Learn the PCA basis from the centered data provided.

Methods inherited from class org.openimaj.math.matrix.algorithm.pca.PrincipalComponentAnalysis

buildNormalisedDataMatrix, generate, getBasis, getCumulativeEnergies, getCumulativeEnergy, getEigenValue, getEigenValues, getEigenVectors, getMean, getPrincipalComponent, getStandardDeviations, getStandardDeviations, learnBasis, learnBasis, learnBasis, project, project, selectSubset, selectSubset, selectSubsetEnergyThreshold, selectSubsetPercentageEnergy, toString

Methods inherited from class java.lang.Object

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

Constructor Detail

ThinSvdPrincipalComponentAnalysis

public ThinSvdPrincipalComponentAnalysis(int ndims)

Construct a ThinSvdPrincipalComponentAnalysis that will extract the n best eigenvectors.

Parameters:

ndims - the number of eigenvectors to select.

Method Detail

learnBasisNorm

public void learnBasisNorm(Jama.Matrix data)

Description copied from class: PrincipalComponentAnalysis

Learn the PCA basis from the centered data provided. Each row corresponds to an observation with the number of dimensions equal to the number of columns.

Specified by:

learnBasisNorm in class PrincipalComponentAnalysis