RecLVQ: Recurrent Learning Vector Quantization
Jensun Ravichandran, Thomas Villmann, Marika Kaden · ESANN 2021 proceedings · 2021
Learning Vector Quantizers (LVQ) and its cost-functionbased variant called Generalized Learning Vector Quanitzation (GLVQ) are powerful, yet simple and interpretable classification models.Even though GLVQ is an effective tool for classifying vectorial data, it cannot handle raw sequence data of potentially different lengths.Usually, this problem is solved by manually engineering fixed-length features or by employing recurrent networks.Therefore, a natural idea is to incorporate recurrent units for data processing into the GLVQ network structure.The processed data can then be compared in a latent space for classification decisions.We demonstrate the ability of this approach on illustrative classification problems.* M.K. and J.R. are