摘要
针对局部线性嵌入(LLE)算法和最大间距准则(MMC)算法在特征提取问题中存在不足,提出一种有效的数据降维和分类方法——基于最大间距准则的局部图嵌入特征提取算法,并将其应用在人脸识别上.该算法在保持近邻的前提下,分别构造类内紧致图和类间惩罚图.首先在类内紧致图中利用线性重构的局部对称性找出高维数据空间中的非线性结构,使同类样本尽可能地聚集在一起;然后在类间惩罚图中使不同类别的样本尽可能分离;为了避免"小样本"问题,采用MMC的形式构造目标函数.在ORL,Yale和AR人脸图像库进行实验的结果表明,文中算法相对于DLA和LLE+LDA算法有较好的识别性能.
To tackle the insufficiency problem of local linear embedding（LLE） algorithm and maximum margin criterion（MMC） algorithm in feature extraction,an efficient dimensional reduction and classification algorithm,local graph embedding feature extraction method based on maximum margin criterion（LGEMMC）,is presented with applications in face recognition.The goal of this algorithm was to construct the intrinsic graph and penalty graph,with the preservation of nearest neighbor premise.In the intrinsic graph,the nonlinear structure is discovered in the high dimensional data space by the local symmetry of the linear restructuring,which causes the similar samples gathering together as much as possible.At the same time,different class samples are as far as possible from each other in the penalty graph.In this method,the ＂small size sample＂ problem is solved by the employment of MMC and the neighborhood relationship is better described by an adequate modification of the adjacency matrix.The results of face recognition experiments on ORL,Yale and AR face databases demonstrate the effectiveness of the proposed method in comparison with the DLA and LLE＋LDA method.
出处
《计算机辅助设计与图形学学报》
EI
CSCD
北大核心
2011年第7期1224-1231,共8页
Journal of Computer-Aided Design & Computer Graphics
基金
国家自然科学基金重点项目(60632050)
国家自然科学基金(60873151)
高等学校博士学科点专项科研基金(20060288013)
关键词
局部线性嵌入
数据降维
人脸识别
最大间距准则
局部图嵌入
local linear embedding
dimensional reduction
face recognition
maximum margin criterion
local graph embedding
