Learning Vector Quantization and relevances in complex coefficient space

Three classes of heartbeats that were learnt in complex wavelet space and backtransformed to the time domain


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.

Neural Computing and Applications
Michiel Straat
Postdoc in Machine Learning

My research interests include Machine Learning, Computational Intelligence and Statistical Physics of Learning.