Publication

Prototypes and matrix relevance learning in complex fourier space

Straat, M., Kaden, M., Gay, M., Villmann, T., Lampe, A., Seiffert, U., Biehl, M. & Melchert, F. 31-Aug-2017 12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM). IEEEXplore, p. 1-6 6 p.

Research output: Scientific - peer-reviewConference contribution

Documents

  • Prototypes and Matrix Relevance

    Final publisher's version, 464 KB, PDF-document

DOI

In this contribution, we consider the classification of time-series and similar functional data which can be represented in complex Fourier 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 makes possible 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.
Original languageEnglish
Title of host publication12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)
PublisherIEEEXplore
Pages1-6
Number of pages6
ISBN (Electronic)978-1-5090-6638-4
StatePublished - 31-Aug-2017
Event12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM) - Nancy, France

Conference

Conference12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)
CountryFrance
CityNancy
Period28/06/201730/06/2017

Event

12th International Workshop on Self-Organizing Maps and Learning Vector Quantization, Clustering and Data Visualization (WSOM)

28/06/201730/06/2017

Nancy, France

Event: Conference

    Keywords

  • Calculus, Discrete Fourier transforms, Frequency-domain analysis, Prototypes, Time series analysis, Time-domain analysis, Training, Classification, Learning Vector Quantization, dimensionality reduction, functional data, relevance learning, supervised learning

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