A Property of LVQ using Model Data

Abstract

The authors classified serum uric acid values in health examination data by SOM significance method. Kohonen's LVQ (Learning vector quantization) is a method for classification. LVQ does not require a normal distribution of data. On the other hand, the multivariate discriminant analysis method is based on a normal distribution of data, In the case of the classification where data is not normally distributed, LVQ could have a better accuracy. Parameter setting such as the number of codebook vectors is arbitrary for LVQ. It is an advantage but could be a disadvantage oppositely. In this study, we report the basic properties of LVQ by using model data.