Classifying Three-way Seismic Volcanic Data by Dissimilarity Representation

Multi-way data analysis is a multivariate data analysis technique having a wide application in some fields. Nevertheless, the development of classification tools for this type of representation is incipient yet. In this paper we study the dissimilarity representation for the classification of three-way data, as dissimilarities allow the representation of multi-dimensional objects in a natural way. As an example, the classification of seismic volcanic events is used. It is shown that in this application classification based on 2D spectrograms, dissimilarities perform better than on 1D spectral features.