1) Client Specific Linear discriminant analysis
客户相关线性判别分析
2) Client Specfic kernel discriminant analysis
客户相关核判别分析
3) Client Specific Kernel Discriminant Analysis(CSKDA)
客户相关的核判别分析
1.
An improved face verification algorithm is proposed based on the modular 2DPCA and Client Specific Kernel Discriminant Analysis(CSKDA) because of the disadvantage of CSKDA.
针对客户相关的核判别分析(CSKDA)对图像列向量进行处理数据维数大、计算复杂,对图像整体处理没有考虑到局部特征等缺点,提出M2DPCA和CSKDA结合的方法。
4) uncorrelated linear discriminant analysis
非相关线性判别分析
1.
Classification and feature selection of proteomic data by uncorrelated linear discriminant analysis
非相关线性判别分析用于蛋白质组数据的分类及特征挑选(英文)
5) Linear discrimination analysis
线性判别分析
1.
A robust dynamic visual feature extraction method based on Bayesian tangent shape model(BTSM) and linear discrimination analysis(LDA) is proposed.
引入一种基于贝叶斯切线形状模型(BTSM)的口形轮廓特征提取和基于线性判别分析(LDA)的视觉语音动态特征提取方法,该特征充分体现了口形特征变化的动态性,消除了直接口形轮廓几何特征的冗余。
6) linear discriminate analysis
线性判别分析
1.
Textiles of EC and China origin were distinctively classified by linear discriminate analysis(LDA) based on the data of extractable heavy metals,and the extractable heavy metals having great effects on the classification were explained by linear discriminate function;the main factors which determined the discrepancy of textiles between the t.
按照Oeko-Tex—100标准对35种产自欧盟的生态纺织品及69种国产染色纺织品的可萃取重金属含量进行测定;利用线性判别分析对被考察的欧盟及国产纺织品进行整体分类,确定了影响分类的主要重金属;利用因子分析对欧盟及国产纺织品在可溶出重金属上的主要决定因素进行分析,通过样本点在线性判别函数空间及因子分析空间的得分,了解被考察国产纺织品与欧盟生态纺织品在可溶出重金属上的整体质量差异,并找出导致差异的主要因素。
2.
The main method of feature extraction is principle component analysis and linear discriminate analysis,but all of them are not perfect.
针对人脸识别系统中的主成分分析和线性判别分析两种特征提取方法的优缺点,提出了一个融合特征提取方法,并构造了一个能够将图像数据空间的人脸映射到人脸特征空间中并实施识别的实验系统。
补充资料：相关分析(见统计分析)
相关分析(见统计分析)
x Iangguan fenxj相关分析见统计分析。
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