图最优化线性鉴别投影及其在图像识别中的应用

Graph-Optimized Linear Discriminant Projection and Its Application to Image Recognition

在图最优化局部保持投影(GoLPP)算法的基础上,本文充分利用数据的类别信息,提出一种新的特征抽取算法——图最优化线性鉴别投影(GoLDP).与GoLPP类似,GoLDP的邻接图是通过最优化一个目标函数创建的;与GoLPP不同,GoLDP利用数据的类别信息创建两幅最优邻接图——最优内在图和最优惩罚图,由这两幅最优邻接图求得最优投影矩阵.FERET与YALE人脸数据库以及PolyU掌纹数据库上的实验结果证明了GoLDP算法的有效性.

The class information of the data is sufficiently utilized and a feature extraction algorithm is proposed called graph-optimized linear discriminant projection （GoLDP） based on graph-optimized locality preserving projection （GoLPP）. The graph of GoLDP is constructed by optimizing an objective function, which is similar to GoLPP. GoLDP constructs two optimal graphs （optimal intrinsic graph and optimal penalty graph） by using class information, which is different from GoLPP, and obtains the optimal projection matrix according to these two optimal graphs. databases and the PolyU palmprint database demonstrate Experimental results on FERET and YALE face the effectiveness of GoLDP.