New Learning Strategy for Prototypes in Linear Vector Quantization Network

This paper proposes a new strategy for prototypes
initialization in linear vector quantization algorithm (LVQ).
Three principles which must be satisfied during the learning
stage are shown in the paper. These principles are essential to
guarantee appropriate learning for the LVQ algorithm.
However, all versions of LVQ algorithms try to answer to one of
the principles, but unfortunately contradicting with the other
ones. The new strategy proposed in the paper aims to solve this
issue and consists of two steps: (1) analyse the a-priori data set
and (2) apply a pre-learning algorithm to initialize the
prototypes. The pre-learned prototypes resulted from step 2 are
used by the LVQ algorithm in the learning process. The
examples presented in the case study and the criterion used to
assess the training performance of the prototypes reinforce that
the training strategy of the prototypes proposed in the paper
provides better results in certain situations compared to classical
LVQ algorithms.