Application of kernel learning vector quantization to novelty detection

In this paper, we focus on kernel learning vector quantization (KLVQ) for handling novelty detection. The two key issues are addressed: the existing KLVQ methods are reviewed and revisited, while the reformulated KLVQ is applied to tackle novelty detection problems. Although the calculation of kernelising the learning vector quantization (LVQ) may add an extra computational cost, the proposed method exhibits better performance over the LVQ. The numerical study on one synthetic data set confirms the benefit in using the proposed KLVQ.