%0 Journal Article
%T Discriminant analysis based on lifting wavelet transform and learning vector quantization<br>基于提升小波变换与学习矢量量化网络的鉴别分析方法
%A CHEN Lei
%A HUANG Xian-wu
%A LIU Jia-sheng
%A ZHONG Xing-rong
%A <br>陈蕾
%A 黄贤武
%A 刘家胜
%A 仲兴荣
%J 计算机应用
%D 2006
%I 
%X A new discriminant analysis method based on LWT(Lifting Wavelet Transform) and LVQ(Learning Vector Quantization) Network was proposed in this paper.LWT is faster and more efficient than the first generation wavelet transform,but it also has the multi-resolution characteristics.LWT can be used to extract the low frequency coefficients and reduce the dimension of an image.LVQ is an effective learning algorithm that trains the competitive layer under supervision.It has a simple network structure,but it also has good discriminant analysis ability.The experimental results on ORL face database show that the method proposed has very good classification capability and high recognition rate.
%K LWT(Lifting Wavelet Transform)
%K LVQ(Learning Vector Quantization)
%K discriminant analysis
%K neural network
%K face recognition<br>提升小波变换
%K 学习矢量量化
%K 鉴别分析
%K 神经网络
%K 人脸识别
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=67B076D6D60894E1&yid=37904DC365DD7266&vid=96C778EE049EE47D&iid=9CF7A0430CBB2DFD&sid=438F607B4D053FEF&eid=2D8A2D26AFF207D2&journal_id=1001-9081&journal_name=计算机应用&referenced_num=1&reference_num=7