HOSVD-wavelet based framework for multidimensional data approximation

András Rövid, László Szeidl, Szabolcs Sergyán, Péter Várlaki

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

The representation of data plays significant role in many applications, as for instance when performing data compression, feature extraction or enhancement, etc. In this paper we briefly mention some well known data representation forms and propose a new domain based on the so called higher order singular value decomposition (HOSVD) and wavelet transformation. It will be shown how the data can be processed by manipulating its components in this domain. Furthermore, the properties of the components as well as the applicability of the proposed approach in the field of image processing and system identification will be shown.

Original languageEnglish
Title of host publicationICCC 2013 - IEEE 9th International Conference on Computational Cybernetics, Proceedings
Pages29-33
Number of pages5
DOIs
Publication statusPublished - Nov 7 2013
EventIEEE 9th International Conference on Computational Cybernetics, ICCC 2013 - Tihany, Hungary
Duration: Jul 8 2013Jul 10 2013

Publication series

NameICCC 2013 - IEEE 9th International Conference on Computational Cybernetics, Proceedings

Other

OtherIEEE 9th International Conference on Computational Cybernetics, ICCC 2013
CountryHungary
CityTihany
Period7/8/137/10/13

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications

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    Rövid, A., Szeidl, L., Sergyán, S., & Várlaki, P. (2013). HOSVD-wavelet based framework for multidimensional data approximation. In ICCC 2013 - IEEE 9th International Conference on Computational Cybernetics, Proceedings (pp. 29-33). [6617625] (ICCC 2013 - IEEE 9th International Conference on Computational Cybernetics, Proceedings). https://doi.org/10.1109/ICCCyb.2013.6617625