%0 Journal Article
%T Robust Linear Embeding Based on a Kernel Function<br>基于核函数的稳健线性嵌入方法
%A XU Xue-song
%A SONG Dong-ming
%A ZHANG Xu
%A XU Man-wu
%A LIU Feng-yu
%A XU Xue-song
%A SONG Dong-ming
%A ZHANG Xu
%A XU Man-wu
%A LIU Feng-yu
%A XU Xue-song
%A SONG Dong-ming
%A ZHANG Xu
%A XU Man-wu
%A LIU Feng-yu
%A XU Xue-song
%A SONG Dong-ming
%A ZHANG Xu
%A XU Man-wu
%A LIU Feng-yu
%A XU Xue-song
%A SONG Dong-ming
%A ZHANG Xu
%A XU Man-wu
%A LIU Feng-yu
%A <br>徐雪松
%A 宋东明
%A 张 谞
%A 许满武
%A 刘凤玉
%J 中国图象图形学报
%D 2009
%I 
%X As a new unsupervised learning method, Local Linear Embedding algorithm(LLE)aims at reducing the nonlinear dimensionality.Since the local linear embedding method has many disadvantages, a new method, namely robust linear embedding method based on a kernel function, is presented to solve this problem. Firstly, the kernel function is utilized to adjust the Euclidean distance between data points, so the new method can improve the performance and the range of application of LLE. Secondly, the new method using the improved W is selected because it is insensitive to noise. It is shown that the actual computation of the subspace is reduced to a standard eigenvalue problem. The proposed method was tested and evaluated in the Yale face database and AT&T face database. Nearest neighborhood (NN)algorithm was used to construct classifiers. The experimental results showed that the improved algorithm has good performance when pose, lighting condition, face expression and train sample number change.
%K manifold learning
%K high dimensional data
%K dimensionality reduction
%K kernel function<br>流形学习
%K 高维数据
%K 维数约减
%K 核函数
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=3B535073C2AA5F9C207DEDBED1CB69AC&yid=DE12191FBD62783C&vid=F3583C8E78166B9E&iid=B31275AF3241DB2D&sid=39B73ADA6F3DD05F&eid=008520E0B52E94B3&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=1&reference_num=12