3) 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。
4) 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)。
5) kernel Fisher discriminant analysis
核Fisher鉴别分析
1.
Feature extraction based on rough kernel Fisher discriminant analysis and its application on aeroengine fault diagnosis;
基于粗糙核Fisher鉴别分析的特征提取及其在发动机故障诊断中的应用
2.
Based on the idea of isomorphic mapping, We proposed an optimal kernel Fisher discriminant analysis (OKFDA), from which we acquire a general algorithm for the computation of the .
基于同构映射的思想,我们提出了一种最优的核Fisher鉴别分析(OKFDA)方法,从理论上巧妙的解决了奇异情况下最优鉴别矢量集的求解问题。
6) 2D-FDA
二维Fisher鉴别分析
1.
The paper gives a method that combined Two Dimensional Fisher Discrimant analysis(2D-FDA) and Proximal SVM in face recognition.
对人脸图像进行二维Fisher鉴别分析(2D-FDA)的特称抽取与最临近支持向量机(Proximal SVM)的分类进行组合。
补充资料：模糊
1.亦作"模胡"。 2.不分明;不清楚。 3.谓草率，马虎。 4.混淆。
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