%0 Journal Article
%T Algorithm for Data Visualization by Hybridizing Neural Gas Network and Sammon’s Mapping<br>混合Neural-Gas网络和Sammon映射的数据可视化算法
%A Jin Liang-nian Ouyang Shan
%A <br>晋良念
%A 欧阳缮
%J 电子与信息学报
%D 2008
%I 
%X Compared with Self-Organizing Feature Map(SOFM), maximum-entropy clustering and K-means clustering, the Neural-Gas network algorithm has advantages of faster convergence, smaller cost distortion errors, etc. However, the fixed and regular neurons on the output space represent worse distance information when the neural gas network algorithm is used for dimension reduction and visualization of linear or nonlinear data sets with nonuniform distribution. Therefore, according to the basic idea of the probabilistic regularized SOFM, a new visualization method for hybridizing neural gas network and Sammon’s mapping is proposed to overcome this problem, and it reduces the computational complexity with using neural gas network algorithm for feature clustering and preserves the interneuronal distances resemblance from input space into output space by using Sammon’s mapping. Simulation results show that the proposed hybridizing algorithm can obtain the better visualization effect on the synthetic and real data sets, thus demonstrating the feasibility and effectiveness of the hybridizing algorithm.
%K Neural-Gas network
%K Sammon’s mapping
%K Hybridizing algorithm
%K Distances resemblance<br>Neural-Gas网络
%K Sammon映射
%K 混合算法
%K 距离相似性
%K 混合算法
%K 网络
%K 特征映射
%K 数据
%K 可视化算法
%K Network
%K Neural
%K Data
%K Visualization
%K Algorithm
%K 有效性
%K 验证
%K 效果
%K 合成
%K 仿真结果
%K 相似性
%K 距离信息
%K 元间
%K 神经元
%K 输入空间
%K 计算复杂度
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=AD531E3D8C50E4E55D6F918DE1973F18&yid=67289AFF6305E306&vid=340AC2BF8E7AB4FD&iid=94C357A881DFC066&sid=C92F92E9A15D3512&eid=F88EF6DD822B0FF5&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=0&reference_num=7