org.openimaj.math.matrix.algorithm.whitening

Class PCAWhitening

java.lang.Object

org.openimaj.math.matrix.algorithm.whitening.WhiteningTransform

org.openimaj.math.matrix.algorithm.whitening.PCAWhitening

Direct Known Subclasses:

ZCAWhitening

@Reference(type=Book,
           author={"Hyvrinen, Aapo","Hurri, Jarmo","Hoyer, Patrick O."},
           title="Natural Image Statistics: A Probabilistic Approach to Early Computational Vision.",
           year="2009",
           edition="1st",
           publisher="Springer Publishing Company, Incorporated",
           customData={"isbn","1848824904, 9781848824904"})
public class PCAWhitening
extends WhiteningTransform

Whitening based on PCA. The PCA basis is used to rotate the data, and then the data is normalised by the variances such that it has unit variance along all principal axes.

Optionally, you can also reduce the dimensionality of the data during the whitening process (by throwing away components with small eignevalues).

Author:

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

Field Summary

Fields

Modifier and Type	Field and Description
protected double	eps
protected int	ndims
protected Normaliser	ns
protected Jama.Matrix	transform

Constructor Summary

Constructors

Constructor and Description
PCAWhitening(double eps, Normaliser ns) Construct with the given variance regularization parameter and data normalisation strategy.
PCAWhitening(double eps, Normaliser ns, int ndims) Construct with the given variance regularization parameter, data normalisation strategy and target dimensionality.

Method Summary

Modifier and Type	Method and Description
Jama.Matrix	getTransform() Get the underlying whitening transform matrix.
void	train(double[][] data) Train the whitening transform with the given features.
double[]	whiten(double[] vector) Apply the whitening transform to the given vector.

Methods inherited from class org.openimaj.math.matrix.algorithm.whitening.WhiteningTransform

whiten

Methods inherited from class java.lang.Object

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

Field Detail

eps

protected double eps

ns

protected Normaliser ns

transform

protected Jama.Matrix transform

ndims

protected int ndims

Constructor Detail

PCAWhitening

public PCAWhitening(double eps,
                    Normaliser ns)

Construct with the given variance regularization parameter and data normalisation strategy.

Parameters:

eps - the variance normalisation regularizer (each principle dimension is divided by sqrt(lamba + eps), where lamba is the corresponding eigenvalue).

ns - the normalisation to apply to each input data vector prior to training the transform or applying the actual whitening.

PCAWhitening

public PCAWhitening(double eps,
                    Normaliser ns,
                    int ndims)

Construct with the given variance regularization parameter, data normalisation strategy and target dimensionality.

Parameters:

eps - the variance normalisation regularizer (each principle dimension is divided by sqrt(lamba + eps), where lamba is the corresponding eigenvalue).

ns - the normalisation to apply to each input data vector prior to training the transform or applying the actual whitening.

ndims - the number of output dimensions for the whitened data

Method Detail

whiten

public double[] whiten(double[] vector)

Description copied from class: WhiteningTransform

Apply the whitening transform to the given vector.

Specified by:

whiten in class WhiteningTransform

Parameters:

vector - the vector

Returns:

the whitened vector

train

public void train(double[][] data)

Description copied from class: WhiteningTransform

Train the whitening transform with the given features.

Specified by:

train in class WhiteningTransform

Parameters:

data - the data (one feature per row)

getTransform

public Jama.Matrix getTransform()

Get the underlying whitening transform matrix.

Returns:

the matrix