Direct linear discriminant analysis based on column pivoting QR decomposition and economic SVD

A direct linear discriminant analysis algorithm based on economic singular value decomposition （DLDA/ESVD） is proposed to address the computationally complex problem of the conventional DLDA algorithm, which directly uses ESVD to reduce dimension and extract eigenvectors corresponding to nonzero eigenvalues. Then a DLDA algorithm based on column pivoting orthogonal triangular （QR） decomposition and ESVD （DLDA/QR-ESVD） is proposed to improve the performance of the DLDA/ESVD algorithm by processing a high-dimensional low rank matrix, which uses column pivoting QR decomposition to reduce dimension and ESVD to extract eigenvectors corresponding to nonzero eigenvalues. The experimental results on ORL, FERET and YALE face databases show that the proposed two algorithms can achieve almost the same performance and outperform the conventional DLDA algorithm in terms of computational complexity and training time. In addition, the experimental results on random data matrices show that the DLDA/QR-ESVD algorithm achieves better performance than the DLDA/ESVD algorithm by processing high-dimensional low rank matrices.

Kernel Model Applied in Kernel Direct Discriminant Analysis for the Recognition of Face with Nonlinear Variations

A kernel-based discriminant analysis method called kernel direct discriminant analysis is employed, which combines the merit of direct linear discriminant analysis with that of kernel trick. In order to demonstrate its better robustness to the complex and nonlinear variations of real face images, such as illumination, facial expression, scale and pose variations, experiments are carried out on the Olivetti Research Laboratory, Yale and self-built face databases. The results indicate that in contrast to kernel principal component analysis and kernel linear discriminant analysis, the method can achieve lower （7%） error rate using only a very small set of features. Furthermore, a new corrected kernel model is proposed to improve the recognition performance. Experimental results confirm its superiority （1% in terms of recognition rate） to other polynomial kernel models.

Unsupervised Linear Discriminant Analysis

An algorithm for unsupervised linear discriminant analysis was presented. Optimal unsupervised discriminant vectors are obtained through maximizing covariance of all samples and minimizing covariance of local k-nearest neighbor samples. The experimental results show our algorithm is effective.

Discriminant Analysis for Human Arm Motion Prediction and Classifying

The EMG signal which is generated by the muscles activity diffuses to the skin surface of human body. This paper presents a pattern recognition system based on Linear Discriminant Analysis (LDA) algorithm for the classification of upper arm motions;where this algorithm was mainly used in face recognition and voice recognition. Also a comparison between the Linear Discriminant Analysis (LDA) and k-Nearest Neighbor (k-NN) algorithm is made for the classification of upper arm motions. The obtained results demonstrate superior performance of LDA to k-NN. The classification results give very accurate classification with very small classification errors. This paper is organized as follows: Muscle Anatomy, Data Classification Methods, Theory of Linear Discriminant Analysis, k-Nearest Neighbor (kNN) Algorithm, Modeling of EMG Pattern Recognition, EMG Data Generator, Electromyography Feature Extraction, Implemented System Results and Discussions, and finally, Conclusions. The proposed structure is simulated using MATLAB.

基于FastICA-LDA的光伏并网逆变器故障诊断

为了实现逆变器开路故障诊断,提出了一种新的诊断方法。该方法采用快速独立成分分析算法判定逆变器是否发生单管开路故障,如果发生单管开路故障,计算旋转电流Id频域下的特征值,将这些特征值作为线性判别分析模型的输入值,最后由LDA模型输出逆变器工作状态编号,从而实现单管开路定位。经过MATLAB仿真验证表明,所提方法对光伏并网逆变器故障的诊断效果较好。

基于LDA-MURE模型的背景音乐自适应推荐方法

用户的情绪状态不同,需要的背景音乐也不同,因此提出基于LDA-MURE模型的背景音乐自适应推荐方法。提取背景音乐的音频特征和社会化标签,通过Fisher线性判别分析方法融合上述数据的特征,结合投影变换方法获得不同类别背景音乐的类内离散度和类间离散度。通过现代心理学分析人类情绪的节律周期变化,在此基础上判断用户当前的情绪状态。最后在LDA模型的基础上构建LDA-MURE模型,为用户推荐不同类别的背景音乐。实验结果表明,所提方法的MEA指标值较低、P@N指标值较高、用户满意度较高。

基于PCA-LDA-SVM算法的茶小绿叶蝉识别

为提高茶小绿叶蝉病虫害的识别效率和精度,提出一种基于PCA-LDA-SVM的茶小绿叶蝉病虫害识别方法。首先,对采集的茶叶图像进行预处理,得到缩放后的图像;然后,利用主成分分析(PCA)对预处理后的图像提取全局特征,降低特征数据的维度,从而减少后续的计算时间;再利用线性判别分析(LDA)寻找特征数据的最优投影空间,使类内散布距离最小,类间散布距离最大,进一步提高识别的准确率和精确度;最后,利用支持向量机(SVM)分类器进行分类识别。试验结果表明,PCA-LDA-SVM模型识别准确率达96%,精确度达100%,召回率达92%,整体识别性能优于SVM,BP,KNN,PCA-SVM模型,具备一定的理论价值和参考意义。

基于LDA-IBES-RELM的光伏阵列故障诊断方法

针对光伏阵列故障诊断准确率偏低的问题,提出了一种基于改进秃鹰搜索算法(IBES)优化正则化极限学习机(RELM)的故障诊断方法。首先在Simulink建立光伏阵列仿真模型,模拟典型故障并提取故障特征数据,同时利用线性判别分析(LDA)对特征量降维作为故障诊断模型的输入;其次利用Logistic混沌映射、Levy飞行策略和柯西高斯变异扰动策略对秃鹰算法进行改进;最后将IBES用于对RELM的隐层参数寻优。实验结果表明:LDA-IBES-RELM模型与BES-RELM、IBES-RELM模型对比,得到的故障诊断准确率为97.71%,优于其他两种模型,验证了LDA-IBESRELM模型用于光伏阵列故障诊断的有效性和实用性。

基于direct LDA的幅度谱子空间雷达目标识别

针对高分辨距离像(HRRP)可分性低和维数高的问题,提出一种新的雷达自动目标识别(RATR)方法:dLDA&SVM。先采用直接线性判别分析在HRRP的幅度谱空间进行特征提取,然后在子空间中采用角域均值模板库训练one-against-all支撑向量机(SVM)多类分类器进行目标识别。并设计了最短距离分类器与SVM分类器比较。基于外场实测数据的实验结果表明,与LDA幅度谱子空间法,幅度谱原空间法相比,dLDA&SVM可显著降低数据维数并提高识别性能。

基于ICEEMDAN和IMWPE-LDA-BOA-SVM的齿轮箱损伤识别模型

针对齿轮箱振动信号中的背景噪声过大影响故障特征质量,进而降低故障识别准确率的问题,提出了一种基于改进自适应噪声完备集成经验模态分解(ICEEMDAN)、改进多尺度加权排列熵(IMWPE)、利用线性判别分析(LDA)、蝴蝶优化算法(BOA)优化支持向量机(SVM)的齿轮箱故障诊断方法(ICEEMDAN-IMWPE-LDA-BOA-SVM)。首先,采用ICEEMDAN对齿轮箱振动信号进行了分解,生成了一系列从低频到高频分布的本征模态函数分量;接着,基于相关系数筛选出包含主要故障信息的本征模态函数分量,进行了信号重构,降低了信号的噪声;随后,提出了改进多尺度加权排列熵的非线性动力学指标,并利用其提取了重构信号的故障特征,以构建反映齿轮箱故障特性的故障特征;然后,利用线性判别分析(LDA)对原始故障特征进行了压缩,以构建低维的故障特征向量;最后,采用蝴蝶优化算法(BOA)对支持向量机(SVM)的惩罚系数和核函数参数进行了优化,以构建参数最优的故障分类器,对齿轮箱的故障进行了识别;基于齿轮箱复合故障数据集对ICEEMDAN-IMWPE-BOA-SVM方法进行了实验和对比分析。研究结果表明:该方法能够较为准确地识别齿轮箱的不同故障类型,准确率达到了99.33%,诊断时间只需5.31 s,在多个方面都优于其他对比方法,在齿轮箱的故障诊断中更具有应用潜力。

核Direct LDA子空间高光谱影像地物分类

为降低高光谱影像的数据维数,提高地物分类识别效率,提出了一种地物分类方法——核直接线性判别分析(Kernel Direct Linear Discriminant Analysis,KDLDA)子空间法;并推导出类先验概率的一般形式下KDLDA的解。KDLDA子空间法先采用KDLDA提取遥感影像的非线性可分特征,然后在KDLDA子空间采用最小距离分类器进行分类识别。机载可见光/红外成像光谱仪(Airborne Visible/Infrared Imaging Spectrometer,AVIRIS)的高光谱影像识别结果表明,相比原空间法、LDA子空间法、直接线性判别分析(Direct Linear Discriminant Analysis,DLDA)子空间法、核线性判别分析(Kernel Linear Discriminant Analysis,KLDA)子空间法,KDLDA子空间法可显著提高识别效率。

基于Direct LDA的相关向量机遥感图像分类

相关向量机是一种新型的基于贝叶斯统计学习框架的有监督机器学习算法,但是存在对高光谱数据分类精度不高的问题。针对此问题,文中提出了一种改进的算法,该算法将直接线性判别分析法与相关向量机相结合,对高光谱数据进行特征提取,然后采用相关向量机进行遥感图像分类。文中实验数据采用1992年Indian Pines高光谱数据。实验结果显示,与采用相关向量机直接分类,线性判别分析法(LDA)与相关向量机结合的两种方法的分类结果相比,文中算法能够明显降低数据维度,总体分类精度也提升了约1%。

基于改进DFA和LDA的永磁同步电机机械故障检测

为提高故障检测的精度,研究了变转速工况下永磁同步电机的机械故障检测方法.首先,分析了电机轴承、转子偏心及其复合故障的振动特性;其次,采用Vold-Kalman算法对故障特征分量进行跟踪提取,并通过信号重构消除转速变化对故障特征分量的影响;提出一种基于改进去趋势波动分析和线性判别式分析的机械故障检测方法,实现对重构信号的故障特征提取和故障检测;最后,对所提出故障检测方法的有效性进行实验验证.实验结果表明文中所提出方法的故障检测精度为88%.

突发事件网络舆情反转的PCA-LDA-LSSVM预测模型

为有效引导与控制突发事件网络舆情,建立科学的预警机制,提出突发事件网络舆情反转的主成分分析(PCA)-线性判别分析(LDA)-最小二乘支持向量机(LSSVM)预测模型。利用PCA提取具有相关性的影响因素主成分,利用LDA方法分析相互独立的影响因素和主成分对突发事件网络舆情反转的影响,并将LDA分析后的影响因素作为LSSVM的输入向量,预测突发事件网络舆情反转,通过选取33组突发事件网络舆情数据进行试验研究。研究结果表明:影响因素重要性由大到小依次为网民情感正倾向、网民情感负倾向、舆情事件性质、舆情传播形式、舆情首发主体权威性;当网民情感正倾向明显减少、网民情感负倾向明显增加时,应采取措施引导舆情发展。

基于表面肌电信号的LDA-BPNN双臂手势识别算法

针对基于表面肌电(sEMG)信号的双臂手势识别率不高的问题,提出一种利用线性判别分析(LDA)方法结合反向传播神经网络(BPNN)算法的手势识别方法。首先,对采集的双臂sEMG信号进行小波阈值去噪的预处理,提取信号中的均方根值、绝对值均值、过零点次数、立方均值、波长、平均绝对值斜率共6种特征;再通过LDA对高维特征集进行降维处理;最后,利用BPNN建立相应的手势模型并识别。实验结果表明:在双臂手势动作的背景下,该识别算法效率较高,识别准确率高达92.7%,能够有效实现双臂手势识别。

An Optimization Criterion for Generalized Marginal Fisher Analysis on Undersampled Problems

Marginal Fisher analysis （MFA） not only aims to maintain the original relations of neighboring data points of the same class but also wants to keep away neighboring data points of the different classes.MFA can effectively overcome the limitation of linear discriminant analysis （LDA） due to data distribution assumption and available projection directions.However,MFA confronts the undersampled problems.Generalized marginal Fisher analysis （GMFA） based on a new optimization criterion is presented,which is applicable to the undersampled problems.The solutions to the proposed criterion for GMFA are derived,which can be characterized in a closed form.Among the solutions,two specific algorithms,namely,normal MFA （NMFA） and orthogonal MFA （OMFA）,are studied,and the methods to implement NMFA and OMFA are proposed.A comparative study on the undersampled problem of face recognition is conducted to evaluate NMFA and OMFA in terms of classification accuracy,which demonstrates the effectiveness of the proposed algorithms.

基于LDA降维和BP神经网络的手写数字识别

手写数字数据集是机器学习分类领域的优质数据集,文章以反向传播(Back Propagation,BP)神经网络为基础,对手写数字进行分类识别。为减少BP神经网络的计算开支,实验前,对比了过滤卡方检验法、主成分分析(Principal Component Analysis,PCA)降维、线性判别式分析(Linear Discriminant Analysis,LDA)降维以及多维尺度变换(Multidimensional Scaling,MDS)降维对特征选取的训练测试效果,从而确定了神经网络拟合之前的最优特征提取方法。实验中,利用Bagging对BP神经网络进行集成处理,分类识别了手写数字。实验后,将文中方法与朴素贝叶斯、决策树、随机森林、LDA多分类进行对比。结果表明,采取LDA降维方法时,降到9维的特征提取方式最优,单个BP神经网络对手写数字数据识别的准确率为92%左右,而基于Bagging集成的BP神经网络在识别准确率方面高达95%。

基于近红外光谱技术的六大茶类快速识别

为构建高质量的六大茶类识别模型,本研究中收集了370份样品,通过采集其近红外光谱(near-infrared spectroscopy,NIRS),结合光谱预处理、特征提取以及数据挖掘分类器算法,建立六大茶类快速识别模型。结果表明:1)支持向量机(support vector machine,SVM)与随机森林(random forest,RF)分类器皆适于六大茶类快速识别模型的构建;2)SVM分类器更适于结合原始光谱(original spectrum,OS)建模,预处理易使基于该分类器建立的模型鉴别性能减弱;3)随机森林(RF)分类器更适用于预处理后光谱建模,所得模型较OS模型在识别正确率(recognition accuracy,RA)及受试者工作特征曲线下面积(area under the curve,AUC)均得到明显提升;4)特征提取中线性判别分析(linear discriminant analysis,LDA)算法表现最好,所得模型的RA较OS模型明显提升,其中最佳模型OS-LDA-SVM的RA为100.00%,AUC为1.00,识别正确率高、泛化能力强、模型性能优异,可产业化应用。综上所述,近红外光谱结合预处理、特征提取算法及分类器建立模型,进行六大茶类识别的可行性强,模型的识别正确率高、性能优异,可为茶叶贸易的茶类快速识别提供科学、准确、高效的技术支撑,为国际茶类识别模型的产业化应用奠定基础。

Recognition for avian influenza virus proteins based on support vector machine and linear discriminant analysis

Total 200 properties related to structural characteristics were employed to represent structures of 400 HA coded proteins of influenza virus as training samples. Some recognition models for HA proteins of avian influenza virus (AIV) were developed using support vector machine (SVM) and linear discriminant analysis (LDA). The results obtained from LDA are as follows: the identification accuracy (Ria) for training samples is 99.8% and Ria by leave one out cross validation is 99.5%. Both Ria of 99.8% for training samples and Ria of 99.3% by leave one out cross validation are obtained using SVM model, respectively. External 200 HA proteins of influenza virus were used to validate the external predictive power of the resulting model. The external Ria for them is 95.5% by LDA and 96.5% by SVM, respectively, which shows that HA proteins of AIVs are preferably recognized by SVM and LDA, and the performances by SVM are superior to those by LDA.

一种稳健的基于VisemicLDA的口形动态特征及听视觉语音识别

视觉特征提取是听视觉语音识别研究的热点问题。文章引入了一种稳健的基于Visemic LDA的口形动态特征,这种特征充分考虑了发音时口形轮廓的变化及视觉Viseme划分。文章同时提出了一利利用语音识别结果进行LDA训练数据自动标注的方法。这种方法免去了繁重的人工标注工作,避免了标注错误。实验表明,将'VisemicLDA视觉特征引入到听视觉语音识别中,可以大大地提高噪声条件下语音识别系统的识别率;将这种视觉特征与多数据流HMM结合之后,在信噪比为10dB的强噪声情况下,识别率仍可以达到80％以上。