%0 Journal Article
%T Overview of nonlinear dimensionality reduction methods in manifold learning<br>流形学习中非线性维数约简方法概述
%A HUANG Qi hong
%A LIU Zhao
%A <br>黄启宏
%A 刘钊
%J 计算机应用研究
%D 2007
%I 
%X A detailed retrospection was made on nonlinear dimensionality reduction methods in manifold learning, whose advantages and defects were pointed out respectively. Compared with traditional linear method, nonlinear dimensionality reduction methods in manifold learning could discover the intrinsic dimensions of nonlinear high-dimensional data effectively, help researcher to reduce dimensionality and analyzer data better, Finally, the prospect of nonlinear dimensionality reduction methods in manifold learning was discussed, so as to extend the application area of manifold learning.
%K dimensional reduction
%K manifold learning
%K multidimensional scaling(MDS)
%K isomap
%K Laplacian eigenmap
%K locally linear embedding(LLE)
%K local tangent space alignment(LTSA)<br>维数约简
%K 流形学习
%K 多维尺度
%K 等距映射
%K 拉普拉斯特征映射
%K 局部线性嵌入
%K 局部切空间排列
%K 流形学习
%K 中非
%K 线性维数
%K 约简方法
%K learning
%K manifold
%K methods
%K dimensionality
%K reduction
%K nonlinear
%K 应用
%K 期望
%K 未来研究方向
%K 数据分析
%K 维数约简
%K 本质维数
%K 高维数据
%K 非线性
%K 发现
%K 比较
%K 优势和不足
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=C793235A4E87B8F6000058BCF2120F7B&yid=A732AF04DDA03BB3&vid=B91E8C6D6FE990DB&iid=708DD6B15D2464E8&sid=2A8D03AD8076A2E3&eid=C5154311167311FE&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=1&reference_num=80