Multiscale information provides an opportunity to improve the outcomes of data analysis processes. However, if the multiscale information is not properly summarized in a compact representation, this may lead to problems related to high dimensional data. In addition, in some situations, it is convenient to define dissimilarities directly for the multiscale data obtaining in this way a multiscale dissimilarity representation. When these dissimilarities are specifically designed for the problem, it is even possible that they do not fulfill metric requirements. Therefore, standard statistical analysis techniques may not be easily applicable. We propose a new method to combine non-metric multiscale dissimilarities in a compact representation which is used for classification. The method is based on the extended multiscale dissimilarity space and prototype selection, which allows us to handle the potentially non-metric nature of the dissimilarities and exploit the multiscale information at the same time. This is achieved in such a way that the most informative examples per scale are selected. Experimental results show that the approach is promising since it finds a better trade-off in accuracy and efficiency than its counterpart approaches.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Subtitle of host publication21st Iberoamerican Congress, CIARP 2016, Proceedings
EditorsCesar Beltrán-Castañón, Ingela Nyström, Fazel Famili
Place of PublicationCham
PublisherSpringer
Pages150-157
Number of pages8
ISBN (Electronic)978-3-319-52277-7
ISBN (Print)978-3-319-52276-0
DOIs
Publication statusPublished - 2017
EventCIARP 2016: 21st IberoAmerican Congress on Pattern Recognition - Lima, Peru
Duration: 8 Nov 201611 Nov 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10125 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceCIARP 2016
CountryPeru
CityLima
Period8/11/1611/11/16

    Research areas

  • Extended multiscale dissimilarity space, Genetic algorithms, Multiscale data, Prototype selection

ID: 44971594