1) Foley Sammon linear discriminant analysis
FoleySammon线性鉴别分析
2) linear discriminant analysis
线性鉴别分析
1.
Fisher linear discriminant analysis(LDA) and Maximum Scatter Difference Discriminate Analysis(MSDDA) are firstly adopted to extract two sets of features in the same pattern space,respectivel.
为了有效地融合Fisher线性鉴别分析与最大散度差鉴别分析所抽取的特征,得到更加全面反映原始样本的鉴别特征集,提出了基于典型相关分析的增强线性鉴别分析方法。
2.
Based on linear discriminant analysis a arithmetic was proposed.
基于线性鉴别分析原理,给出了一个拟合度判断算法。
3.
Uncorrelated discriminant analysis is a very effective method for linear discriminant analysis and plays an important role in discriminant analysis.
不相关鉴别分析是一种非常有效并起着重要作用的线性鉴别分析方法,它能抽取出具有不相关性质的特征分量。
3) linear discriminant analysis(LDA)
线性鉴别分析
1.
Direct LDA(DLDA) is an extension of Linear Discriminant Analysis(LDA) to deal with the small sample size problem,which is previously claimed to take advantage of all the information,both within and outside of the within-class scatter\'s null space.
直接线性鉴别分析(DLDA)是一种以克服小样本问题而提出的LDA扩展方法,被声明利用了包含类内散布矩阵零空间外的所有信息。
4) linear discriminant analysis(LDA)
线性鉴别分析(LDA)
5) Fisher linear discriminant analysis
Fisher线性鉴别分析
1.
Cosidering the so-called "Small Sample Size"(SSS) problem in nature and the "inferior" problem in traditional Fisher linear discriminant analysis, a new method of feature extraction based on modified maximum scatter-difference criterion is developed in this paper.
针对传统的Fisher线性鉴别分析在人脸这样的多类高维小样本模式的分类中存在的"小样本问题"和"次优性问题",该文提出了一种基于修正的最大散度差鉴别准则的线性鉴别分析方法。
2.
Fisher linear discriminant analysis(LDA),a well-known feature extraction method,searches for the projection axes on which the data samples from different classes are far from each other while requiring data samples of the same class to be close to each other.
作为一种著名的特征抽取方法,Fisher线性鉴别分析的基本思想是选择使得Fisher准则函数达到最大值的向量(称为最优鉴别向量)作为最优投影方向,以便使得高维输入空间中的模式样本在该向量投影后,在类间散度达到最大的同时,类内散度最小。
3.
These methods include principal component analysis (PCA), Fisher linear discriminant analysis (FLD), statistically independent linear discriminant analysis, Adaboost algorithm, and support vector machine (SVM) .
系统地研究了不同的特征提取方法和分类方法在性别分类问题上的性能,其中包括主分量分析(PCA)、Fisher线性鉴别分析(FLD)、最佳特征提取、Adaboost算法、支持向量机(SVM)。
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