%0 Journal Article
%T Algorithm for Sammon''''s nonlinear mapping based on fuzzy kernel learning vector quantization<br>基于模糊核LVQ的Sammon非线性映射算法
%A JIN Liang-nian
%A OU Yang-shan
%A LI Min-zheng
%A <br>晋良念
%A 欧阳缮
%A 李民政
%J 计算机应用
%D 2007
%I 
%X 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.
%K nonlinear mapping
%K Sammon's projection
%K distance preservation
%K computational complexity
%K fuzzy kernel
%K Learning Vector Quantization (LVQ)<br>非线性映射
%K Sammon投影
%K 距离保持性
%K 计算复杂度
%K 模糊核
%K 学习矢量量化
%K 模糊核
%K 学习矢量量化
%K 非线性
%K 映射算法
%K Algorithm
%K vector
%K quantization
%K learning
%K fuzzy
%K kernel
%K based
%K 可靠性和稳定性
%K 结果验证
%K 仿真
%K 相似
%K 信息保持
%K 距离
%K 聚类中心
%K 复杂度
%K 计算
%K 核映射
%K 数据样本
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=14E16CBD274305121A517B6A9CBD40F0&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=38B194292C032A66&sid=4AB4178709047BE3&eid=AF4A4411BB448A36&journal_id=1001-9081&journal_name=计算机应用&referenced_num=0&reference_num=8