Adaptive basis functions for prototype-based classification of functional data

Friedrich Melchert*, Gabriele Bani, Udo Seiffert, Michael Biehl

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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Abstract

We present a framework for distance-based classification of functional data. We consider the analysis of labeled spectral data and time series by means of generalized matrix relevance learning vector quantization (GMLVQ) as an example. To take advantage of the functional nature, a functional expansion of the input data is considered. Instead of using a predefined set of basis functions for the expansion, a more flexible scheme of an adaptive functional basis is employed. GMLVQ is applied on the resulting functional parameters to solve the classification task. For comparison of the classification, a GMLVQ system is also applied to the raw input data, as well as on data expanded by a different predefined functional basis. Computer experiments show that the methods offer potential to improve classification performance significantly. Furthermore, the analysis of the adapted set of basis functions give further insights into the data structure and yields an option for a drastic reduction of dimensionality.

Original languageEnglish
Pages (from-to)18213-18223
Number of pages11
JournalNeural Computing and Applications
Volume32
Issue number24
Early online date13-Jul-2019
DOIs
Publication statusPublished - Dec-2020

Keywords

  • Adaptive basis
  • Functional data
  • GMLVQ
  • Machine learning
  • Relevance learning

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