双向压缩的2DPCA与PCA相结合的人脸识别算法

主成分分析(PCA)直接用于人脸识别时,需将图像矩阵转换成向量,导致求解高阶矩阵计算量大.二维主成分分析(2DPCA)的实质是对图像矩阵按行进行图像压缩抽取特征,消除了图像列的相关性,但特征教量仍然较大,影响分类速度.针对这一问题,提出了采用双向压缩的二维主成分分析消除图像行间和列间的相关性,再结合PCA进一步减少特征数量,改进人脸识别算法,该算法用于ORL人脸库上得到了较高的识别率和较快的识别速度.     

浅谈基于PCA的网络流量分析

网络流量的特性分析一直是通信网络性能分析的一个极其重要的问题。本文主要采用主成分分析(PCA)的方法对采样到的网络流量数据进行分析,发现这些流量数据呈现低维特性,仿真结果证明该方法可行。     

主成分在输电线路可听噪声预测中的应用研究

针对超高压输电线路可听噪声BP网络预测模型影响因素多的问题,运用主成分分析算法（PCA）对影响可听噪声的环境因素、地理参数、导线结构参数等14个因素进行简化,建立PCA-BP网络预测模型。选取甘肃省内多条750 kV、330 kV输电线路的可听噪声的实测资料为样本集,采用Matlab神经网络工具箱进行模型训练与预测,并与BP网络模型预测结果比较。结果表明：主成分分析方法在可听噪声影响因素的简化中不适用,预测结果没有BP网络模型预测结果理想。分析了主成分在可听噪声影响因素简化中不适用的原因。     

A comparison of three methods for principal component analysis of fuzzy interval data

Vertices Principal Component Analysis (V-PCA), and Centers Principal Component Analysis (C-PCA) generalize Principal Component Analysis (PCA) in order to summarize interval valued data. Neural Network Principal Component Analysis (NN-PCA) represents an extension of PCA for fuzzy interval data. However, also the first two methods can be used for analyzing fuzzy interval data, but they then ignore the spread information. In the literature, the V-PCA method is usually considered computationally cumbersome because it requires the transformation of the interval valued data matrix into a single valued data matrix the number of rows of which depends exponentially on the number of variables and linearly on the number of observation units. However, it has been shown that this problem can be overcome by considering the cross-products matrix which is easy to compute. A review of C-PCA and V-PCA (which hence also includes the computational short-cut to V-PCA) and NN-PCA is provided. Furthermore, a comparison is given of the three methods by means of a simulation study and by an application to an empirical data set. In the simulation study, fuzzy interval data are generated according to various models, and it is reported in which conditions each method performs best.     

图像矩阵上的广义最大噪声分离算法

主成分分析（PCA）是模式识别中一种重要的变换工具,在图像处理的特征提取和降维方面有广泛的应用。然而,由于二维图像数据需要进行向量化处理,导致高维向量的产生和像素空间位置丢失。广义主成分分析（GPCA）则是基于图像矩阵的主成分分析推广算法,它不改变像素间的空间位置关系,而且计算量也显著降低。但主成分分析和广义主成分分析都没有考虑到实际图像中存在的噪声干扰。最大噪声分离（MNF）则是一种面向噪声干扰的变换方法,与主成分分析基于方差的最大化不同,最大噪声分离是基于信噪比的最大化。与GPCA的推广类似,在图像二维矩阵上推广最大噪声分离方法,提出一种广义最大噪声分离（GMNF）算法。该变换方法在保证重构时信噪比最大的同时,也具有不改变像素空间位置、计算量小的优点。在人脸和红外图像上的仿真实验结果验证了所提算法的有效性。     

基于子空间方法的大规模网络流量异常检测

本文采用子空间方法和PCA（主成分分析或Principal Components Analysis）对大规模网络流量异常检测进行研究,并以校园网为实验环境,应用子空间方法和PCA实现了网络流量异常检测。通过实验结果与小波分析结果的对比,证明了基于子空间方法的大规模网络流量异常检测是一种既简单又高效的方法。     

一种基于张量PCA的人耳识别的改进方法

张量主成分分析是一种新的主元分析方法,可以解决传统PCA方法对图像进行降维时出现的问题。小波变换具有良好的时频分析特性,同时还能起到降维的作用。综合利用这两个方法的优点,提出了一种基于张量PCA的人耳识别新方法。该方法对人耳图像采用小波变换做预处理得到4个子带图像,对其中“LL”低频子带图像用张量PCA进行特征提取,用支持向量机的方法进行识别。实验结果表明,利用此方法与传统主成分分析识别相比,提高了识别率,缩短了识别时间。在USTB人耳库上实验,该方法的识别率比传统PCA方法提高了6%,识别时间为传统PCA方法的35.23%。     

Online identification of nonlinear system using reduced kernel principal component analysis

The Principal Component Analysis (PCA) is a powerful technique for extracting structure from possibly high-dimensional data sets. It is readily performed by solving an eigenvalue problem, or by using iterative algorithms that estimate principal components. This paper proposes a new method for online identification of a nonlinear system modelled on Reproducing Kernel Hilbert Space (RKHS). Therefore, the PCA technique is tuned twice, first we exploit the Kernel PCA (KPCA) which is a nonlinear extension of the PCA to RKHS as it transforms the input data by a nonlinear mapping into a high-dimensional feature space to which the PCA is performed. Second, we use the Reduced Kernel Principal Component Analysis (RKPCA) to update the principal components that represent the observations selected by the KPCA method.     

基于主成分分析的拒绝服务和网络探测攻击检测

针对拒绝服务和网络探测攻击难以检测的问题,提出了一种新的基于主成分分析的拒绝服务和网络探测攻击检测方法。首先在攻击流量和正常流量数据集上应用主成分分析,得到所有流量数据集的各种不同统计量;然后依据这些统计量构造攻击检测模型。实验表明：该模型检测拒绝服务和网络探测攻击的检测率达到99%;同时能够让受攻击对象在有限的时间内做出反应,减少攻击对服务器的危害程度。     

A Neural Network for PCA and Beyond

Principal Component Analysis (PCA) has been implemented by several neural methods. We discuss a Network which has previously been shown to find the Principal Component subspace though not the actual Principal Components themselves. By introducing a constraint to the learning rule (we do not allow the weights to become negative) we cause the same network to find the actual Principal Components. We then use the network to identify individual independent sources when the signals from such sources are ORed together.     

Diagnosing Traffic Anomalies Using a Two-Phase Model

Network traffic anomalies are unusual changes in a network,so diagnosing anomalies is important for network management.Feature-based anomaly detection models (ab)normal network traffic behavior by analyzing packet header features.PCA-subspace method (Principal Component Analysis) has been verified as an efficient feature-based way in network-wide anomaly detection.Despite the powerful ability of PCA-subspace method for network-wide traffic detection,it cannot be effectively used for detection on a single link.In this paper,different from most works focusing on detection on flow-level traffic,based on observations of six traffic features for packet-level traffic,we propose a new approach B6SVM to detect anomalies for packet-level traffic on a single link.The basic idea of B6-SVM is to diagnose anomalies in a multi-dimensional view of traffic features using Support Vector Machine (SVM).Through two-phase classification,B6-SVM can detect anomalies with high detection rate and low false alarm rate.The test results demonstrate the effectiveness and potential of our technique in diagnosing anomalies.Further,compared to previous feature-based anomaly detection approaches,B6-SVM provides a framework to automatically identify possible anomalous types.The framework of B6-SVM is generic and therefore,we expect the derived insights will be helpful for similar future research efforts.     

Weighted Modular Image Principal Component Analysis for face recognition

This paper proposes two feature extraction techniques that minimizes the effects of distortions generated by variations in illumination, rotation and, head pose in automatic face recognition systems. The proposed techniques are Modular IMage Principal Component Analysis (MIMPCA) and weighted Modular Image Principal Component Analysis (wMIMPCA). Both techniques are based on PCA and they use the modular image decomposition to minimize local variation. Also, the covariance matrix is calculated directly from the original image matrix. This strategy generates a smaller matrix compared with traditional PCA and reduces the computational effort. wMIMPCA assumes that parts of the face are more discriminatory than others, so a Genetic Algorithm is used to obtain weights for each region in the face image. The proposed techniques are compared with Modular PCA and two-dimensional PCA using three well-known databases, showing better results.     

基于核函数的PCA在QAR数据分析中的应用

分析了传统的主成分分析方法的不足,论述了KPCA方法及其时间复杂度高的缺陷。在此基础上,提出基于核函数构造的协方差矩阵的主成分分析,相比 KPCA,该方法具有快的降维速度。实验结果显示：把该方法用于QAR数据具有良好的降维效果和高分类正确率。     

基于多线性独立成分分析的掌纹识别

为快速有效地在掌纹识别中学习多种因素的高阶统计独立成分,利用多线性独立成分分析方法对掌纹张量进行降维,得到低维的模式矩阵,将掌纹图像向模式矩阵上投影以提取核心张量,通过计算核心张量间的余弦距离实现掌纹匹配。基于PolyU掌纹图像库的实验结果表明,与主成分分析(PCA)、二维PCA、独立成分分析和多线性PCA相比,该方法的识别率最高,且满足系统实时性要求。     

基于NSCT域主分量分析的遥感图像去噪方法

提出一种新的结合非下采样Contourlet变换（NSCT）和主分量分析（PCA）的图像自适应阈值去噪方法。通过PCA估计NSCT域中的噪声能量,并与NSCT系数的领域信息相结合,构造出自适应阈值对遥感图像进行去噪。仿真实验结果表明,提出的方法与Contourlet硬阈值,基于Contourlet的图像PCA和NSCT硬阈值去噪方法相比能够有效去除遥感图像的高斯噪声,较完整地保持图像的边缘等细节信息,提高了图像的峰值信噪比,图像视觉效果也有明显改善。     

基于PSO-BP神经网络的企业信用风险评估模型研究

企业信用风险评估是金融领域的重要课题.本文针对单独运用BP神经网络评估信用风险时存在的不足,提出了一种基于PSO-BP神经网络的企业信用风险评估模型.该模型首先应用主成分分析方法降低输入BP网络的信用评估指标维数,并且采用粒子群优化算法优化BP神经网络的权值.实验表明,新模型采用的算法具有收敛速度快,预测精度高的优点,是一种有效可靠的企业信用风险评估模型.     

神经网络集成的城市道路状态判别模型研究

针对城市道路交通状态影响因素多、判别难的特点,在分析K-均值聚类算法和概率神经网络（PNN）的基础上,利用多源检测信息的互补性,提出一种基于快速全局聚类分析的概率神经网络集成模型,通过聚类提高集成网络间的差异度,同时利用主成分分析（PCA）优化概率神经网络结构,仿真实验表明该模型与传统的集成方法Bagging相比,能够利用更简单的网络结构,快速有效地识别出城市道路交通状态,为交通预警和诱导策略的制定提供数据依据。     

Robust hebbian learning and noisy principal component analysis

The classical analysis of a stochastic signal into principal components compresses the signal using an optimal selection of linear features. Noisy Principal Component Analysis (NPCA) is an extension of PCA under the assumption that the extracted features are unreliable, and the unreliability is modeled by additive noise. The applications of this assumption appear for instance, in communications problems with noisy channels. The level of noise in the NPCA features affects the reconstruction error in a way resembling the water-filling analogy in information theory. Robust neural network models for Noisy PCA can be defined with respect to certain synaptic weight constraints. In this paper we present the NPCA theory related to a particularly simple and tractable constraint which allows us to evaluate the robustness of old PCA Hebbian learning rules. It turns out that those algorithms are not optimally robust in the sense that they produce a zero solution when the noise power level reaches half the limit set by NPCA. In fact, they are not NPCA-optimal for any other noise levels except zero. Finally, we propose new NPCA-optimal robust Hebbian learning algorithms for multiple adaptive noisy principal component extraction.     

基于最大熵投票模型的时间序列无监督分割

通过高维时间序列分割可以创建高级符号表示。提出一种针对高维时间序列的无监督分割算法,用于解决高维数据符号化的预处理问题。该算法实现对高维数据的聚类,应用最大熵投票模型进行序列分割。实验结果表明,其平均查全率和查准率分别为0.86和0.88,且整体性能优于主成分分析算法和概率主成分分析算法。     

基于余弦角距离的主成分分析与核主成分分析

PCA和KPCA都是基于欧氏距离提出的,这种距离对离群数据点比较敏感,而余弦角距离对离群数据更为鲁棒,在很多情况下具有更好的性能。充分利用余弦角距离的优势,提出两种新的特征抽取算法——基于余弦角距离的主成分分析（PCAC）和基于余弦角距离的核主成分分析（KPCAC）。在YALE人脸数据库与PolyU掌纹数据库上的实验表明,PCAC比PCA取得了更好的效果,KPCAC也表现出了很好的性能。