1) Two-Dimensional LDA(2DLDA)
二维线性鉴别分析(2DLDA)
2) two-dimensional linear discriminant analysis
2维线性鉴别分析(2DLDA)
1.
On the basis of two-dimensional linear discriminant analysis(2DLDA),a novel discriminant analysis named two-dimensional heteroscedastic discriminant analysis(2DHDA)is introduced,and is used for face recognition.
在2维线性鉴别分析(2DLDA)的基础上,介绍了2维异方差鉴别分析(2DHDA),并将其应用于人脸识别。
3) fuzzy two-dimensional linear discriminant analysis(F2DLDA)
模糊二维线性鉴别分析
4) weighted 2DLDA
加权二维线性鉴别分析
1.
Face recognition with single training sample per person based on generalized slide window and weighted 2DLDA;
然后,使用加权二维线性鉴别分析方法(Weighted2DLDA)对上面获得的窗口图像进行特征抽取。
5) Bilateral Two-Dimensional Linear Discriminant Analysis(B-2DLDA)
双向二维线性鉴别分析
1.
A novel method based on Bilateral Two-Dimensional Linear Discriminant Analysis(B-2DLDA) and symmetry average of local Singular Value Decomposition(SVD) for face recognition is presented.
针对人脸识别中光照、表情、姿态的影响,提出一种融合双向二维线性鉴别分析和局部对称平均的人脸识别新方法。
6) Bilateral Two-Dimensional Linear Discriminant Analysis(B2DLDA)
双向二维线性鉴别分析方法
补充资料：多元线性回归分析
在线性相关条件下，研究两个或两个以上自变量对一个因变量的数量变化关系，称为多元线性回归分析。
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条