Algorithm for Sammon''''s nonlinear mapping based on fuzzy kernel learning vector quantization
基于模糊核LVQ的Sammon非线性映射算法

An new algorithm for Sammon's nonlinear kernel mapping based on reliable and stable fuzzy kernel learning vector quantization was presented. The data space was mapped to high dimension feature space with Mercer kernel function, and fuzzy kernel learning vector quantization (FKLVQ) was done on the feature space to obtain the effective and stable clustering weight vectors. Finally Sammon's nonlinear kernel mapping only for the data points and the clusters was executed on the output space and the feature space, thus reducing computational complexity and preserving the distance resemblance between the clusters and the data points from the data space to the output space. Simulation results demonstrate the reliability and stability of the proposed algorithm.