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Computing Dissimilarities, Manipulation , Visualization
Dissimilarity Matrix Classification, Dissimilarity Space, PE-Embedding, Evaluation

Manipulation of dissimilarity matrices

This page belongs to the User Guide of the DisTools Matlab package. It describes some of its commands. Links to other pages are listed above. More information can be found in the pages of the  PRTools User Guide. Links are given at the bottom of this page.

This group of commands contains routines that either change specific entries of a dissimilarity matrix, or generate subsets. The construction of such a subset affects both, the rows as well as the columns of a dissimilarity matrix, i.e. it selects some objects and adapts their representation.

The input dissimilarity matrices should be square: rows and columns should point to the same objects. Moreover, in the dataset the set of object labels should be identical to the set of feature labels. See the FAQ on square dissimilarities.

dissimt Transforms similarities into dissimilarities or vice versa.
makesym Make a dissimilarity matrix symmetric
D2= D*makesym default is averaging D and D'.
D2= D*makesym([],'min') use min(D,D')
makemetric Make a square dissimilarity matrix metric
D2 = D*makemetric All dissimilarities in D that violate the triangle inequality are updated
genddat Generate random training and test sets for dissimilarity data,
[DT,DS] = genddat(D,0.5) use 50% of the data for trainset (DT), remaining for testset (DS); repset is trainset
[DT,DS] = genddat(D,[10 20],5) use 10 objects of first class and 20 of second for training. repset is first 5 objects of trainset
 seldclass Select class subset from a square dissimilarity dataset
D2 = seldclass(D,[3 4]) Reduce square dissimilarity matrix such that only rows and columns of classes 3 and 4 are preserved

PRTools User Guide
elements: datasets, datafiles. cells and doubles, mappings, classifiers, mapping types
operations: datasets, datafiles, cells and doubles, mappings, classifiers, stacked, parallel, sequential, dyadic
commands: datasets, representation, classifiers, evaluation, clustering and regression, examples, support

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