public class MDS
extends java.lang.Object
PCA
,
SammonMapping
Constructor and Description 

MDS(double[][] proximity)
Constructor.

MDS(double[][] proximity,
int k)
Constructor.

MDS(double[][] proximity,
int k,
boolean add)
Constructor.

Modifier and Type  Method and Description 

double[][] 
getCoordinates()
Returns the principal coordinates of projected data.

double[] 
getEigenValues()
Returns the component scores, ordered from largest to smallest.

double[] 
getProportion()
Returns the proportion of variance contained in each eigenvectors,
ordered from largest to smallest.

public MDS(double[][] proximity)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.public MDS(double[][] proximity, int k)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.k
 the dimension of the projection.public MDS(double[][] proximity, int k, boolean add)
proximity
 the nonnegative proximity matrix of dissimilarities. The
diagonal should be zero and all other elements should be positive and
symmetric. For pairwise distances matrix, it should be just the plain
distance, not squared.k
 the dimension of the projection.add
 true to estimate an appropriate constant to be added
to all the dissimilarities, apart from the selfdissimilarities, that
makes the learning matrix positive semidefinite. The other formulation of
the additive constant problem is as follows. If the proximity is
measured in an interval scale, where there is no natural origin, then there
is not a sympathy of the dissimilarities to the distances in the Euclidean
space used to represent the objects. In this case, we can estimate a constant c
such that proximity + c may be taken as ratio data, and also possibly
to minimize the dimensionality of the Euclidean space required for
representing the objects.public double[] getEigenValues()
public double[] getProportion()
public double[][] getCoordinates()