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.



Titolo: Classifying Three-way Seismic Volcanic Data by Dissimilarity Representation
Autori: 
Porro Munoz, Diana
mostra contributor esterni 
Data di pubblicazione: 2010
Abstract: 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.
Handle: http://hdl.handle.net/11582/192216
ISBN: 9781424475421
Appare nelle tipologie:4.1 Contributo in Atti di convegno

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