The dissimilarity representation as a tool for three-way data classification: a 2D measure

Authors:
Diana Porro-Muñoz;Robert P. W. Duin;Mauricio Orozco-Alzate;Isneri Talavera;John Makario Londoño-Bonilla
Affiliations:
Advanced Technologies Application Center, Cenatav, Cuba and Pattern Recognition Lab, TU Delft, The Netherlands and Pattern Recognition Lab, TU Delft, The Netherlands;Pattern Recognition Lab, TU Delft, The Netherlands;Universidad Nacional de Colombia Sede Manizales, Colombia;Advanced Technologies Application Center, Cenatav, Cuba and Pattern Recognition Lab, TU Delft, The Netherlands;Instituto Colombiano de Geología y Minería, Ingeominas, Colombia
Venue:
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Year:
2010

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Abstract

The dissimilarity representation has demonstrated advantages in the solution of classification problems. Meanwhile, the representation of objects by multi-dimensional arrays is necessary in many research areas. However, the development of proper classification tools that take the multi-way structure into account is incipient. This paper introduces the use of the dissimilarity representation as a tool for classifying three-way data, as dissimilarities allow the representation of multidimensional objects in a natural way. As an example, the classification of three-way seismic volcanic data is used. A comparison is made between dissimilarity measures used in different representations of the three-way data. 2D dissimilarity measures for three-way data can be useful.