Learning vector quantization and relevances in complex coefficient space

M. Straat, M. Kaden, M. Gay, T. Villmann, A. Lampe, U. Seiffert, M. Biehl, F. Melchert*

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

3 Citaten (Scopus)
234 Downloads (Pure)


In this contribution, we consider the classification of time series and similar functional data which can be represented in complex Fourier and wavelet coefficient space. We apply versions of learning vector quantization (LVQ) which are suitable for complex-valued data, based on the so-called Wirtinger calculus. It allows for the formulation of gradient-based update rules in the framework of cost-function-based generalized matrix relevance LVQ (GMLVQ). Alternatively, we consider the concatenation of real and imaginary parts of Fourier coefficients in a real-valued feature vector and the classification of time-domain representations by means of conventional GMLVQ. In addition, we consider the application of the method in combination with wavelet-space features to heartbeat classification.
Originele taal-2English
Pagina's (van-tot)18085–18099
Aantal pagina's15
TijdschriftNeural Computing and Applications
Nummer van het tijdschrift24
Vroegere onlinedatum9-mrt.-2019
StatusPublished - dec.-2020

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