New Learning Strategy for Prototypes in Linear Vector Quantization Network

Gheorghe Puscasu, Alexandru Stancu, Bogdan Codres, Eduard Codres, Emil Ceanga

    Research output: Contribution to journalArticlepeer-review


    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 another. This situation leads to unacceptable solutions for the LVQ algorithm. 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. In this way, by using pre-learned prototypes, unexpected situations which can arise during the learning stage with the LVQ algorithm are avoided. 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.
    Original languageEnglish
    JournalInternational Journal of Neural Systems
    Publication statusPublished - 2015


    • LVQ algorithms, pattern recognition, learning, Self-Organizing Map algorithm.


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