线性判别分析

基于DCT和线性判别分析的人脸识别

Face Recognition Based on DCT and LDA

基于嘴部Gabor小波特征和线性判别分析的疲劳检测

Yawning Detection Based on Gabor Wavelets and LDA

最后，利用Fisher线性判别分析实现分类。

Finally , Fisher linear discriminant analysis is employed for classification .

Fisher线性判别分析在人脸识别应用中取得了很好效果。

Fisher linear discriminant analysis has obtained successful applications in face recognition .

基于Fisher线性判别分析的人脸表情特征提取。

Facial expression feature extraction based on Fisher linear discriminant analysis ( LDA ) .

本文以信用评级问题为核心，通过采用线性判别分析模型、概率神经网络模型、BP神经网络模型以及支持向量机方法对我国上市公司的信用风险度进行信用评级模型的建立。

Linear-Discriminant analysis , Probabilistic Neural Network , Back Propagation Neural Network and Support Vector Machine are all adopted to develop credit rating models .

所采用的特征融合具体方法为LDA线性判别分析。

The article propose linear discriminant analysis used in features combination .

我们用线性判别分析方法进行分类，用敏感性-特异性曲线（ReceiverOperatingCharacteristicCurves：ROC）评估分类器性能。

Then linear discriminant analysis is used to classify nodules into malignant and benign ones , the discriminant scores are analyzed using Receiver Operating Characteristic Curves ( ROC ) method .

其次，在特征提取环节，重点研究了基于主成分分析（PCA）和线性判别分析（LDA）的线性特征提取方法。

Secondly , in the part of feature extraction , it focuses on PCA and LDA .

本文的第二个工作是研究了线性判别分析（LDA）的区分性变换在说话人识别中的应用。

The second issue is the use of discrimination transform of LDA in speaker recognition .

采用Bayes线性判别分析提出新的预测公式，并利用一系列评价指标及ROC曲线等与OSTA进行比较。

Bayes discriminant analysis was adopted to find a new assessment equation .

本文介绍两类线性判别分析BASIC程序，并以化探中判别未知异常归属为例演示该程序的使用方法。该程序在生产中有一定的实用价值。

This paper introduces two kinds of linear discriminant analysis of Basic Procedure , with unknown abnormal be-longing discriminated in chemical exploration to demonstrate the application method of the procedure , which is of certain practical application value in production .

提出基于支持向量机（SVM）和线性判别分析（LDA）的步态特征提取与身份识别方法。

A new gait recognition method based on support vector machine ( SVM ) and linear discriminant analysis ( LDA ) is proposed .

然后采用典型相关分析（CCA）改进线性判别分析（LDA）中的变换矩阵，使得特征向量的降维具有自适应性；

Then , the LDA transformation matrix is improved with CCA , which makes the reduction of dimensions adaptive .

其中的主成分分析（PCA）和线性判别分析（LDA）经常被用于特征降维。

Among them , principal component analysis ( PCA ) and linear discriminant analysis ( LDA ) are more often used in dimensionality reduction .

对于Fisher线性判别分析这种特征提取方法，在二分类的基础上，讨论得到了多类问题的最优投影矩阵，并且成功的解决了奇异矩阵求伪逆的问题。

As the second method of feature extraction , fisher LDA ( linear discriminate analysis ) is analyzed systemically , then the projection matrix is gained .

线性判别分析（LDA）则是通过最大化数据的类间离散度和类内离散度的比值来选择最优的投影方向，投影后的数据更加具有区分性。

Linear Discriminant Analysis ( LDA ) is to maximize the ratio of between-class scatter and within-class scatter to choose the optimal projection direction .

结果，使得类内离散度矩阵总是奇异的，所以不能直接使用线性判别分析（LDA）方法。

As a consequence , the within-class scatter matrix is singular and the Linear Discriminant Analysis ( LDA ) method cannot be applied directly .

研究了基于Fisher线性判别分析方法，针对人脸识别中的小样本问题本文提出了先用主成分分析（PCA）方法减少特征空间维数，再利用Fisher线性判别方法实现对人脸的识别。

The technique of face recognition based on Fisher linear discriminant are analyzed and studied . Then a subspace method based on PCA and Fisher linear discriminant is proposed here .

首先使用线性判别分析（LDA）或核判别分析（KDA）的方法，寻找类间最大可分离的投影空间。

Firstly , Linear Discriminant Analysis ( LDA ) or Kernel Discriminant Analysis ( KDA ) is used to seek a projection of best separates data .

作者由波谱图建立细纱样本观察值向量，运用Fisher线性判别分析法，得到评定细纱不匀的判别函数。

Using the spectrograph , the authors derive the sample vector of yarn and make use of the Fisher linear discriminative method to obtain linear discriminative functions for assessing the yarn irregularity .

本文测定了一组对位取代苯胺与未经处理或经苯巴比妥诱导的大鼠肝微粒体形成细胞色素P－450代谢中间体络合物的活性，并用Fisher线性判别分析研究了它们的构效关系。

A group of p-substituted anilines were tested on their activities to form cytochrome P450 metabolic intermediate complexes with untreated or phenobarbital-induced rat hepatic microsomes . The Fisher 's linear discriminant analysis method was used to study their structure-activity relationship .

然后再将改进后的主成分分析法和Fisher线性判别分析方法组合起来进行人脸识别，在ORL人脸数据库上进行了实验，识别率得到了明显提高。

Then combines the improved PCA and Fisher linear discrimination to recognize human face . We experimented on ORL face database , the experimental result proves that recognition ratio is higher evidently .

由于局部区域特征维数相对较少，子分类器直接使用FLD进行线性判别分析，能够最大限度保留细节和可判别信息。

Due to the small dimension of each local patches , FLD will be directly applied for training the sub-classifiers which can reserve details and discriminant information maximally .

首先，用改进PCA算法对原样本降维，获得最优特征表示子空间；然后，在保证该子空间类内散度矩阵非奇异的基础上，作改进的线性判别分析。

The improved PCA is used to reduce the dimension first to get the optimal characteristic subspace of the original sample data set . In this process , the intra-class scatter matrix of the characteristic subspace should be ensured to be non-singular .

对Fisher二类线性判别分析方法进行改进，并利用改进的基于Fisher准则的二类非线性判别分析方法处理绵羊毛和山羊绒纤维识别指标，并计算检验判别效果的统计量。

Improve the bi-class linear discriminatory analysis method base on Fisher criteria . Use improved non-linear method to treat the virtue of the sheep wool and the cashmere . Collect the distinguishing index acquired and make a statistics of discriminatory result .

本文结合核方法、主元分析和线性判别分析等机器学习方法，提出了一种特征分析的KPL方法。

This paper investigates kernel method , principal component analysis and linear discriminant analysis algorithms for proposed the KPL features analysis algorithm .

先对面部表情图像进行了分割，得到眼睛和嘴巴区域，然后分别对眼睛和嘴巴区域提取不变矩和奇异值特征向量，并进行Fisher线性判别分析，最后训练了支持向量机分类器。

First , eyes and mouths are segmented from the facial expression image and variant moments and singular value feature vectors of eyes and mouth are extracted . Then Fisher linear discriminant analysis is used to find a set of optimal feature vectors .

提出了一种新的音乐分类方法，该方法使用线性判别分析（LDA）和支持向量机（SVMs）对音乐数据进行分类。

In this paper , a new music classification method is presented . The LDA ( Linear Discriminative Analysis ) and SVMs ( Support Vector Machine ) are used in this method to classify the music data accurately .

对基于主元分析（PCA）、二维主元分析（2DPCA）和Fisher线性判别分析（FLDA）的人脸识别方法进行了比较研究。

Some of face recognition methods based on Principal Component Analysis ( PCA ), Two-dimensional Principal Component Analysis ( 2DPCA ) and Fisher 's Linear Discriminant Analysis ( FLDA ) are comparatively studied in this paper .