Bagging-SVM集成分类器估计头部姿态方法

针对现有常用分类器性能不能满足头部姿态估计对准确率的要求,以及光照变化影响头部姿态估计准确率的问题,提出了一种基于Bagging-SVM集成分类器的头部姿态估计方法。首先,通过图片预处理和Adaboost检测人脸区域算法减少背景、光照等干扰因素对于头部姿态特征提取的影响。其次,采用融合方向梯度直方图(HOG)特征和局部二值模式(LBP)特征分别对人脸的轮廓特征和纹理特征进行提取。然后,通过主成分分析(PCA)对融合的头部姿态特征进行特征选择,抽取其主元特征分量供分类器进行训练。最后,通过Bagging方法构建多个训练数据集,并采用支持向量机(SVM)对每个数据集进行训练,产生多个弱分类器,多个弱分类器投票决定测试样本所属类别。将该算法在Pointing’04数据集、CAS-PEAL-R1数据集和自建数据集上进行验证实验,实验结果表明提出的算法相比线性判别分类器(LDA)、朴素贝叶斯分类器(NB)等常用分类算法具有更高的分类准确率,对光照的变化具有较好的鲁棒性。     

基于Gab6r小波和2DPCA-2DLDA的人脸识别研究

结合Gabor小波、二维主成分分析和二维线性判别分析的特点,提出一种人脸特征提取方法。算法首先对人脸图像进行Gabor小波变换,然后进行2DPCA特征提取,提取特征脸,最后进行2DLDA处理．用支持向量机作为分类器。使用这种方法在ORL、Yale人脸库进行测试结果表明,计算复杂度得到减少的同时识别率也得到提高。     

非线性特征提取和LSSVM在化工过程故障诊断中应用

提出利用非线性特征提取（核主成分分析（KPCA）和核独立成分分析）消除数据的不相关性,降低维数。核主成分分析利用核函数把输入数据映射到特征空间,进行线性主成分分析计算提取特征;核独立成分分析在KPCA白化空间进行线性独立成分分析（ICA）变换提取独立成分。提取的特征作为最小二乘支持向量机分类器的输入,构建融合非线性特征提取和最小二乘支持向量机的智能故障分类方法。研究了该方法应用到某石化企业润滑油生产过程的故障诊断中的有效性和可行性。     

融合全局与局部特征的贝叶斯人脸识别方法

针对人脸识别特征提取阶段中的数据降维方法往往难以兼顾保持全局与局部特征信息的问题，以及匹配识别阶段贝叶斯分类器中小样本问题，提出了一种融合全局与局部特征的贝叶斯人脸识别方法。该方法通过核主元分析提取出人脸数据的全局非线性特征，并在此基础上通过正交化局部敏感判别分析挖掘出人脸数据的局部流形结构信息，以达到提取出具有高判别力低维本质人脸特征的目的；采用一种最大信息量协方差选择的方法，来对协方差矩阵进行估算，以解决贝叶斯分类器设计中的小样本问题。在ORL、AR、 YALE、FLW人脸库上设计实验来进行验证。结果表明，提出的特征提取算法以及对贝叶斯分类器的改进取得了比较好的效果，通过对这两个阶段的优化，可以显著提升人脸识别的效果。     

核参数判别选择方法在核主元分析中的应用

针对核主元分析(KPCA)中高斯核参数β的经验选取问题,提出了核主元分析的核参数判别选择方法。依据训练样本的类标签计算类内、类间核窗宽,在以上核窗宽中经判别选择方法确定核参数。根据判别选择核参数所确定的核矩阵,能够准确描述训练空间的结构特征。用主成分分析(PCA)对特征空间进行分解,提取主成分以实现降维和特征提取。判别核窗宽方法在分类密集区域选择较小窗宽,在分类稀疏区域选择较大窗宽。将判别核主成分分析(Dis-KPCA)应用到数据模拟实例和田纳西过程(TEP),通过与KPCA、PCA方法比较,实验结果表明,Dis-KPCA方法有效地对样本数据降维且将三个类别数据100%分开,因此,所提方法的降维精度更高。     

集成阀门运行故障诊断方法仿真研究

针对集成阀门故障诊断在工业过程控制中的需求以及阀门故障信号特征的非线性和非平稳性的特点,提出了一种融合概率主元分析与改进SVM的集成阀门故障诊断方法。采用概率主元分析(PPCA)对故障样本进行主元特征提取,解决了信号维数难以确定的难题,训练"一对一投票"的多类SVM分类器提高了诊断准确率。实验结果表明,方法不仅有很高的正确诊断率,而且对自相关性较严重的阀门故障信号也有较好的诊断效果,对提高现代工业阀门控制过程的稳定性和持续性有一定的指导意义。     

基于核化原理的非线性典型相关判别分析

典型相关判别分析是将传统的典型相关分析应用于判别问题,它是一类重要的特征提取算法,但其本质上只能提取数据的线性特征,应用统计学习理论中的核化原理可以将这样的线性特征提取算法推广至非线性特征提取算法,该文研究了如何将这一原理应用于典型相关判别分析,提出了基于核化原理的非线性典型相关判别分析,并且给出了求解该问题的一个自适应学习算法．数值实验表明,基于核化原理所导出的非线性典型相关判别分析比传统的典型相关判别分析更有效,另外,该文从理论上证明,所提出的新方法与Fisher核判别分析等价。     

苹果近红外光谱的特征提取和分类研究

采用近红外光谱分析法对不同种类的苹果样品进行分类,提出一种基于非相关判别转换的苹果近红外光谱定性分析新方法。实验分别采用主成分分析、Fisher判别分析和非相关判别转换三种方法对苹果光谱数据进行特征提取,并使用K-近邻分类算法建立三种苹果分类识别模型,最后使用"留一"交叉验证法进行模型检验。结果表明,使用非相关判别转换方法建立的模型正确识别率优于使用主成分分析和Fisher判别分析建立的模型。     

基于多线性主元分析和FLD的掌纹识别*

为了减少高维对计算成本的影响,同时提取有利于分类的判别特征,提出运用多线性主元分析(MPCA)与FLD相结合的方法进行掌纹识别。运用MPCA直接对掌纹张量进行降维和特征提取,低维特征向量作为FLD的输入,提取判别特征向量,计算特征向量间的余弦距离进行掌纹匹配。PolyU掌纹图像库的实验结果表明,与主元分析（PCA）、PCA+FLD、二维主元分析(2DPCA)、独立元分析（ICA）和MPCA相比,该算法的识别率(RR)最高为9991%,特征提取和匹配总时间为0398 s,满足实时系统的要求。     

一种融合KPCA和KDA的人脸识别新方法

核判别分析（KDA）和核主成分分析（KPCA）分别是线性判别分析（LDA）和主成分分析（PCA）在核空间中的非线性推广,提出了一种融合KDA和KPCA的特征提取方法并应用于人脸识别中,该方法综合利用KDA和KPCA 的优点来提高人脸识别的性能。此外,还提出了一种广义最近特征线(GNFL)方法来构造有效的分类器。实验结果证明：提出的方法获得了更好的识别结果。     

A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization

Kernel principal component analysis (KPCA) and kernel linear discriminant analysis (KLDA) are two commonly used and effective methods for dimensionality reduction and feature extraction. In this paper, we propose a KLDA method based on maximal class separability for extracting the optimal features of analog fault data sets, where the proposed KLDA method is compared with principal component analysis (PCA), linear discriminant analysis (LDA) and KPCA methods. Meanwhile, a novel particle swarm optimization (PSO) based algorithm is developed to tune parameters and structures of neural networks jointly. Our study shows that KLDA is overall superior to PCA, LDA and KPCA in feature extraction performance and the proposed PSO-based algorithm has the properties of convenience of implementation and better training performance than Back-propagation algorithm. The simulation results demonstrate the effectiveness of these methods.     

Robust sparse manifold discriminant analysis

Classical linear discriminant analysis (LDA) has been applied to machine learning and pattern recognition successfully, and many variants based on LDA are proposed. However, the traditional LDA has several disadvantages as follows: Firstly, since the features selected by feature selection have good interpretability, LDA has poor performance in feature selection. Secondly, there are many redundant features or noisy data in the original data, but LDA has poor robustness to noisy data and outliers. Lastly, LDA only utilizes the global discriminant information, without consideration for the local discriminant structure. In order to overcome the above problems, we present a robust sparse manifold discriminant analysis (RSMDA) method. In RSMDA, by introducing the L_2,1 norm, the most discriminant features can be selected for discriminant analysis. Meanwhile, the local manifold structure is used to capture the local discriminant information of the original data. Due to the introduction of L_2,1 constraints and local discriminant information, the proposed method has excellent robustness to noisy data and has the potential to perform better than other methods. A large number of experiments on different data sets have proved the good effectiveness of RSMDA.

     

基于规范化KDDA的人脸识别算法

传统的PCA和LDA算法受限于“小样本问题”,且对像素的高阶相关性不敏感。论文将核函数方法与规范化LDA相结合,将原图像空间通过非线性映射变换到高维特征空间,并借助于“核技巧”在新的空间中应用鉴别分析方法。通过对ORL人脸库的大量实验表明,该方法在特征提取方面优于PCA,KPCA,LDA等其他方法,在简化分类器的同时,也可以获得高识别率。     

基于规范化KDDA的人脸识别

传统的PCA和LDA算法受限于“小样本问题”,且对象素的高阶相关性不敏感。本文将核函数方法与规范化LDA相结合,将原图像空间通过非线性映射变换到高维特征空间,并借助于“核技巧”在新的空间中应用鉴别分析方法。通过对ORL人脸库的大量实验研究表明,本文方法在特征提取方面明显优于PCA,KPCA,LDA等其他传统的人脸识别方法,在简化分类器的同时,也可以获得高识别率。     

Feature extraction and dimensionality reduction algorithms and their applications in vowel recognition

Feature extraction is an important component of a pattern recognition system. It performs two tasks: transforming input parameter vector into a feature vector and/or reducing its dimensionality. A well-defined feature extraction algorithm makes the classification process more effective and efficient. Two popular methods for feature extraction are linear discriminant analysis (LDA) and principal component analysis (PCA). In this paper, the minimum classification error (MCE) training algorithm (which was originally proposed for optimizing classifiers) is investigated for feature extraction. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithm. LDA, PCA, and MCE and GMCE algorithms extract features through linear transformation. Support vector machine (SVM) is a recently developed pattern classification algorithm, which uses non-linear kernel functions to achieve non-linear decision boundaries in the parametric space. In this paper, SVM is also investigated and compared to linear feature extraction algorithms.     

Efficient and robust feature extraction by maximum margin criterion

In pattern recognition, feature extraction techniques are widely employed to reduce the dimensionality of data and to enhance the discriminatory information. Principal component analysis (PCA) and linear discriminant analysis (LDA) are the two most popular linear dimensionality reduction methods. However, PCA is not very effective for the extraction of the most discriminant features, and LDA is not stable due to the small sample size problem . In this paper, we propose some new (linear and nonlinear) feature extractors based on maximum margin criterion (MMC). Geometrically, feature extractors based on MMC maximize the (average) margin between classes after dimensionality reduction. It is shown that MMC can represent class separability better than PCA. As a connection to LDA, we may also derive LDA from MMC by incorporating some constraints. By using some other constraints, we establish a new linear feature extractor that does not suffer from the small sample size problem, which is known to cause serious stability problems for LDA. The kernelized (nonlinear) counterpart of this linear feature extractor is also established in the paper. Our extensive experiments demonstrate that the new feature extractors are effective, stable, and efficient.     

Finger-vein pattern identification using SVM and neural network technique

This paper presents a support vector machine (SVM) technique for finger-vein pattern identification in a personal identification system. Finger-vein pattern identification is one of the most secure and convenient techniques for personal identification. In the proposed system, the finger-vein pattern is captured by infrared LED and a CCD camera because the vein pattern is not easily observed in visible light. The proposed verification system consists of image pre-processing and pattern classification. In the work, principal component analysis (PCA) and linear discriminant analysis (LDA) are applied to the image pre-processing as dimension reduction and feature extraction. For pattern classification, this system used an SVM and adaptive neuro-fuzzy inference system (ANFIS). The PCA method is used to remove noise residing in the discarded dimensions and retain the main feature by LDA. The features are then used in pattern classification and identification. The accuracy of classification using SVM is 98% and only takes 0.015 s. The result shows a superior performance to the artificial neural network of ANFIS in the proposed system.     

基于线性投影分析和FCM动态聚类的人脸识别*

依据主成分分析方法(PCA)对图像具有很好的表达能力,即能很好地重构原图像,而线性鉴别分析(LDA)可使图像样本具有较高可分性的特点,提出对图像先进行PCA处理,再进行LDA处理,从而降低人脸特征维数并对人脸图像进行了特征提取;并提出用FCM动态聚类算法作为识别分类器,对人脸进行识别。实验和分析结果表明,在人脸识别中,这种融合PCA和LDA的分类方法能够更好地对特征进行提取,且FCM动态聚类分类器比K近邻判别分类器更具有灵活的分类能力。     

Orthogonal sparse linear discriminant analysis

Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L_2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.     

融合判别式深度特征学习的图像识别算法

目的 卷积神经网络在图像识别算法中得到了广泛应用。针对传统卷积神经网络学习到的特征缺少更有效的鉴别能力而导致图像识别性能不佳等问题,提出一种融合线性判别式思想的损失函数LDloss（linear discriminant loss）并用于图像识别中的深度特征提取,以提高特征的鉴别能力,进而改善图像识别性能。方法 首先利用卷积神经网络搭建特征提取所需的深度网络,然后在考虑样本分类误差最小化的基础上,对于图像多分类问题,引入LDA（linear discriminant analysis）思想构建新的损失函数参与卷积神经网络的训练,来最小化类内特征距离和最大化类间特征距离,以提高特征的鉴别能力,从而进一步提高图像识别性能,分析表明,本文算法可以获得更有助于样本分类的特征。其中,学习过程中采用均值分批迭代更新的策略实现样本均值平稳更新。结果 该算法在MNIST数据集和CK+数据库上分别取得了99.53%和94.73%的平均识别率,与现有算法相比较有一定的提升。同时,与传统的损失函数Softmax loss和Hinge loss对比,采用LDloss的深度网络在MNIST数据集上分别提升了0.2%和0.3%,在CK+数据库上分别提升了9.21%和24.28%。结论 本文提出一种新的融合判别式深度特征学习算法,该算法能有效地提高深度网络的可鉴别能力,从而提高图像识别精度,并且在测试阶段,与Softmax loss相比也不需要额外的计算量。