Divergence Based Learning Vector Quantization
Ernest Mwebaze, Petra Schneider, Frank-Michael Schleif, Sven Haase, Thomas Villmann, Michael L. Biehl · 2010
Abstract. We suggest the use of alternative distance measures for similarity based classification in Learning Vector Quantization. Divergences can be employed whenever the data consists of non-negative normalized features, which is the case for, e.g., spectral data or histograms. As examples, we derive gradient based training algorithms in the framework of Generalized Learning Vector Quantization based on the so-called Cauchy-Schwarz divergence and a non-symmetric Renyi divergence. As a first test we apply the methods to two different biomedical data sets and compare with the use of standard Euclidean distance. 1