Differentiable Kernels in Generalized Matrix Learning Vector Quantization
Marika Kästner, David Nebel, Martin Riedel, Michael L. Biehl, Thomas Villmann · 2012
In the present paper we investigate the application of differentiable kernel for generalized matrix learning vector quantization as an alternative kernel-based classifier, which additionally provides classification dependent data visualization. We show that the concept of differentiable kernels allows a prototype description in the data space but equipped with the kernel metric. Moreover, using the visualization properties of the original matrix learning vector quantization we are able to optimize the class visualization by inherent visualization mapping learning also in this new kernel-metric data space.