1) 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)。
2) Fisher Linear Discriminate Analysis(FLDA)
Fisher线性鉴别分析(FLDA)
3) Kernel Fisher Linear Discriminant Analysis(KFDA)
Fisher非线性鉴别分析
4) Fisher discriminant analysis
Fisher鉴别分析
1.
A multi-goal optimization model for Fisher discriminant analysis is developed.
该文提出一种新的Fisher鉴别分析的多目标优化问题模型。
2.
Though the conventional kernel Fisher discriminant analysis has overcome the nonlinear problems,the limitation of final eigenvectors’dimensions determined by class number still exists.
Fisher鉴别分析被公认为是特征抽取的有效方法之一,但由于其只能抽取线性特征,而对于实际应用中复杂的样本图像分布,抽取非线性鉴别特征显得十分必要。
3.
In this paper,we point out the weakness of the previous methods anda new method of Fisher discriminant analysis with Schur decomposition is pro- posed.
本文提出了一种新的基于Schur分解的Fisher鉴别分析的特征抽取方法。
5) Fisher Linear Discrimination Analysis
Fisher线性鉴别
1.
The Technology of Face Recognition Based on Fisher Linear Discrimination Analysis;
基于Fisher线性鉴别分析的人脸识别方法研究
6) Fisher linear discriminant analysis
Fisher线性判别分析
1.
Finally,Fisher linear discriminant analysis is employed for classification.
首先,对原始光谱进行四级小波分解,选择主要包含谱线信息的第四级小波系数作为光谱的小波特征;然后,利用主分量分析对光谱的小波特征进行特征压缩,得到光谱的识别特征;最后,利用Fisher线性判别分析实现分类。
2.
In this paper, a method for color face recognition is presented, this algorithm extracts the final features by utilizing the techniques of the simulative K-L transform, the singular value decomposition, the principal component analysis and the Fisher linear discriminant analysis.
该算法采用模拟K-L变换、奇异值分解、主分量分析和Fisher线性判别分析技术来提取最终特征,可以使分类器的设计更加简洁、有效,使用较少的特征向量数目就能取得较高的识别率。
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