Article
Combining Dissimilarities for Three-Way Data Classification
Fecha
2011-09-10Registro en:
Revista Computación y Sistemas; Vol. 15 No. 1
1405-5546
Autor
Porro Muñoz, Diana
Talavera, Isneri
W. Duin, Robert P.
Orozco Alzate, Mauricio
Institución
Resumen
Abstract. The representation of objects by multidimensional
arrays is widely applied in many research
areas. Nevertheless, there is a lack of tools to classify
data with this structure. In this paper, an approach for
classifying objects represented by matrices is introduced,
based on the advantages and success of the
combination strategy, and particularly in the dissimilarity
representation. A procedure for obtaining the new
representation of the data has also been developed,
aimed at obtaining a more powerful representation.
The proposed approach is evaluated on two threeway
data sets. This has been done by comparing the
different ways of achieving the new representation,
and the traditional vector representation of the objects.