Two linear subpattern dimensionality reduction algorithms

This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.

Fast Tensor Principal Component Analysis via Proximal Alternating Direction Method with Vectorized Technique

This paper studies the problem of tensor principal component analysis (PCA). Usually the tensor PCA is viewed as a low-rank matrix completion problem via matrix factorization technique, and nuclear norm is used as a convex approximation of the rank operator under mild condition. However, most nuclear norm minimization approaches are based on SVD operations. Given a matrix , the time complexity of SVD operation is O(mn2), which brings prohibitive computational complexity in large-scale problems. In this paper, an efficient and scalable algorithm for tensor principal component analysis is proposed which is called Linearized Alternating Direction Method with Vectorized technique for Tensor Principal Component Analysis (LADMVTPCA). Different from traditional matrix factorization methods, LADMVTPCA utilizes the vectorized technique to formulate the tensor as an outer product of vectors, which greatly improves the computational efficacy compared to matrix factorization method. In the experiment part, synthetic tensor data with different orders are used to empirically evaluate the proposed algorithm LADMVTPCA. Results have shown that LADMVTPCA outperforms matrix factorization based method.

Comparison of Kernel Entropy Component Analysis with Several Dimensionality Reduction Methods

Dimensionality reduction techniques play an important role in data mining. Kernel entropy component analysis( KECA) is a newly developed method for data transformation and dimensionality reduction. This paper conducted a comparative study of KECA with other five dimensionality reduction methods,principal component analysis( PCA),kernel PCA( KPCA),locally linear embedding( LLE),laplacian eigenmaps( LAE) and diffusion maps( DM). Three quality assessment criteria, local continuity meta-criterion( LCMC),trustworthiness and continuity measure(T&C),and mean relative rank error( MRRE) are applied as direct performance indexes to assess those dimensionality reduction methods. Moreover,the clustering accuracy is used as an indirect performance index to evaluate the quality of the representative data gotten by those methods. The comparisons are performed on six datasets and the results are analyzed by Friedman test with the corresponding post-hoc tests. The results indicate that KECA shows an excellent performance in both quality assessment criteria and clustering accuracy assessing.

Description and Classification of Leather Defects Based on Principal Component Analysis

The accurate extraction and classification of leather defects is an important guarantee for the automation and quality evaluation of leather industry. Aiming at the problem of data classification of leather defects,a hierarchical classification for defects is proposed.Firstly,samples are collected according to the method of minimum rectangle,and defects are extracted by image processing method.According to the geometric features of representation, they are divided into dot,line and surface for rough classification. From analysing the data which extracting the defects of geometry,gray and texture,the dominating characteristics can be acquired. Each type of defect by choosing different and representative characteristics,reducing the dimension of the data,and through these characteristics of clustering to achieve convergence effectively,realize extracted accurately,and digitized the defect characteristics,eventually establish the database. The results showthat this method can achieve more than 90% accuracy and greatly improve the accuracy of classification.

Generalized two-dimensional correlation near-infrared spectroscopy and principal component analysis of the structures of methanol and ethanol

Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were computed.Combining near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D analysis.This study will be helpful to the understanding of these reagents.

Fault Diagnosis with Wavelet Packet Transform and Principal Component Analysis for Multi-terminal Hybrid HVDC Network

In view of the fact that the wavelet packet transform(WPT) can only weakly detect the occurrence of fault, this paper applies a fault diagnosis algorithm including wavelet packet transform and principal component analysis(PCA) to the inverter-side fault diagnosis of multi-terminal hybrid highvoltage direct current(HVDC) network, which can significantly improve the speed and accuracy of fault diagnosis. Firstly, current amplitude and current slope are used to sample the data,and the WPT is used to extract the energy spectrum of the signal. Secondly, an energy matrix is constructed, and the PCA method is used to calculate whether the squared prediction error(SPE) statistics of various signals that can reflect the degree of deviation of the measured value from the principal component model at a certain time exceed the limit to judge the occurrence of the fault. Further, its maximum value is compared to determine the fault types. Finally, based on a large number of MATLAB/Simulink simulation results, it is shown that the PCA method using the current slope as the sampled data can detect the occurrence of a ground fault with small transition resistance within 2 ms, and identify the fault types within 10 ms,without being affected by the sampling frequency.

Model-based Predictive Control for Spatially-distributed Systems Using Dimensional Reduction Models

In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.

Identification of the anomaly component using BEMD combined with PCA from element concentrations in the Tengchong tin belt, SW China

Concentration of elements or element groups in a geological body is the result of multiple stages of rockforming and ore-forming geological processes.An ore-forming element group can be identified by PCA(principal component analysis)and be separated into two components using BEMD(bi-dimensional empirical mode decomposition):(1)a high background component which represents the ore-forming background developed in rocks through various geological processes favorable for mineralization(i.e.magmatism,sedimentation and/or metamorphism);(2)the anomaly component which reflects the oreforming anomaly that is overprinted on the high background component developed during mineralization.Anomaly components are used to identify ore-finding targets more effectively than ore-forming element groups.Three steps of data analytical procedures are described in this paper;firstly,the application of PCA to establish the ore-forming element group;secondly,using BEMD on the o re-forming element group to identify the anomaly components created by different types of mineralization processes;and finally,identifying ore-finding targets based on the anomaly components.This method is applied to the Tengchong tin-polymetallic belt to delineate ore-finding targets,where four targets for Sn(W)and three targets for Pb-Zn-Ag-Fe polymetallic mineralization are identified and defined as new areas for further prospecting.It is shown that BEMD combined with PCA can be applied not only in extracting the anomaly component for delineating the ore-finding target,but also in extracting the residual component for identifying its high background zone favorable for mineralization from its oreforming element group.

Nonlinear Dynamic Analysis of MPEG-4 Video Traffic

The main research motive is to analysis and to veiny the inherent nonlinear character of MPEG-4 video. The power spectral density estimation of the video trafiic describes its 1/f^β and periodic characteristics.The priraeipal compohems analysis of the reconstructed space dimension shows only several principal components can be the representation of all dimensions. The correlation dimension analysis proves its fractal characteristic. To accurately compute the largest Lyapunov exponent, the video traffic is divided into many parts.So the largest Lyapunov exponent spectrum is separately calculated using the small data sets method. The largest Lyapunov exponent spectrum shows there exists abundant nonlinear chaos in MPEG-4 video traffic. The conclusion can be made that MPEG-4 video traffic have complex nonlinear be havior and can be characterized by its power spectral density,principal components, correlation dimension and the largest Lyapunov exponent besides its common statistics.

Analysis of Chaotic Characters for the Monthly Runoff Se-ries at Fudedian Station in Liaohe Bain

The evolution of monthly runoff is affected both by climate environment and human activities, and its characteristics play an important role in runoff prediction and simulation. In this paper, the G-P and the principal component analysis method, which are both based on the reconstruction theory of the phase space, are used to study the chaos characteristics of the monthly runoff series at Fudedian station in Liaohe basin. The results show that the monthly runoff series have a large probability of chaos.

基于主成分分析的直播水稻后代稳定品系产量及稻米品质比较

以52份直播后代稳定品系为研究材料,对其稻米产量及品质性状进行分析,结果表明,供试材料产量及稻米品质性状均有不同程度分离。在产量上有待提高,但在加工品质、外观品质、营养食味品质等方面存在较大优势。利用主成分分析对直播稻种质资源稻米品质性状采用降维处理,构建了稻米品质性状的综合评价函数,筛选出5份评分大于2.0的材料,22DS045评分最高,达3.102,为辽宁滨海稻区抗逆优良食味直播水稻新品种选育研究与创新提供材料基础。

四种保健黄酒中的挥发性组分分析

采用顶空固相微萃取结合全二维气相色谱-质谱(Headspace solid-phase microextraction-comprehensive two dimensional gas chromatography/mass spectrometry HS-SPME-GC×GC-MS)技术,对4种保健黄酒(黄精酒、黄米酒、藜麦酒和苦荞酒)中挥发性物质的种类、含量分进行分析,并且通过主成分分析法很好地区分不同原料的保健黄酒,找出重要的组分差异特征,探究其风味成分。结果表明,GC×GC-MS检测到4种保健黄酒中挥发性组分156种,选取匹配度大于800的挥发性组分,4种保健黄酒中共鉴定出140种挥发性组分,其中包括酯类、醇类、醛酮类、酸类、烃类、含氮化合物、苯系芳烃及其它化合物等。该方法可以通过鉴定黄酒挥发性组分,寻找挥发性组分与黄酒品质之间的关系,为保健黄酒的生产优化提供一定的理论依据。

基于相关性分析和生成对抗网络的电网缺失数据填补方法

城市电网新型电力系统中多元资源增多,数据采集难度加大,导致数据随机缺失率升高,难以满足精细化分析决策需求。为解决新型电力系统中配网量测数据在采集与传输过程中频发的缺失问题,文中提出一种基于波动互相关分析(fluctuation cross-correlation analysis,FCCA)算法和生成对抗网络(generative adversarial network,GAN)的电网缺失数据填补方法。首先,融合FCCA算法提出强相关性电网数据多维特征提取方法;其次,基于核主成分分析(kernel principal component analysis,KPCA)对多维特征数据集进行降维处理;最后,设计改进型GAN结构,融合电网数据多维特征对低维向量进行重构,实现缺失数据填补。算例采用真实电网数据进行算法验证,并在某城市电网试运行。结果表明,所提方法比传统数据填补方法具有更高填补精度。因此,在新型电力系统中量测数据连续缺失和缺失量较大的情况下,融合强相关性特征进行数据填补,对提升量测数据的完整性和可用性有明显优势。

捻度配置对双向包覆纱性能的影响及优化

为了纺制兼具回弹性和导电性的包覆纱,探讨捻度配置对包覆纱强伸性能和回弹性能的影响。以氨纶、不锈钢丝、棉股线为原料,基于空心锭包缠纺设备,纺制以氨纶为纱芯、不锈钢丝为第一外包覆层、棉股线为第二外包覆层的双向包覆纱。重点分析包缠捻度、外内捻度比等工艺参数对双向包覆纱的强伸性能、回弹性能的影响,利用主成分分析法对试验结果进行综合评判。结果表明:包缠捻度和外内捻度比对双向包覆纱强伸性能影响显著。在设定范围内,当包缠捻度800捻/m、外内捻度比1.0时,双向包覆纱综合质量较优。双向包覆纱在拉伸态下具有优良的导电特性,拓展了其在柔性可拉伸导线和柔弹电加热器件领域的应用范畴。认为:纺制兼具回弹性和导电性的双向包覆纱时,捻度配置是关键参数。

基于深度子空间学习的焊缝缺陷检测方法

主成分分析网络(PCANet)是一个基于简化的卷积神经网络的深度子空间学习模型。针对PCANet算法应用于焊缝缺陷检测时无法体现数据完整结构信息、对噪声较敏感等问题,在PCANet的基础上提出一种鲁棒非贪婪双向二维PCANet(RNG-BDPCANet)焊缝缺陷在线检测方法。RNG-BDPCANet在范数距离度量标准下,利用双向二维主成分分析作卷积核,并采用非贪婪策略得到目标函数最优的整体投影矩阵,对离群值具有较强的鲁棒性。最后,在自建的焊缝人工数据集、ORL和Yale B人脸数据集上分别进行实验。结果表明,所提出的算法在分类性能方面得到显著提高,具有较强的鲁棒性能。

Principal Model Analysis Based on Bagging PLS and PCA and Its Application in Financial Statement Fraud

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this paper.In the proposed PMA algorithm,the PCA and the Bagging PLS are combined.In this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement approach.The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix.The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix.Subsequently,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and PLS-LDA.Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability.Additionally,it is employed in the application of financial statement fraud identification.PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.

三维荧光结合区域积分鉴别酸枣仁及其掺伪品

目的:研究建立酸枣仁三维荧光光谱鉴别方法,为酸枣仁真伪鉴别提供依据。方法:建立6批不同产地酸枣仁三维荧光光谱,并利用三维荧光技术对2批酸枣仁伪品、3批掺伪品鉴别分析,采用荧光区域积分法对不同产地酸枣仁、酸枣仁伪品及掺伪品三维荧光图谱进行区域积分,将区域积分值进行相似度评价、聚类分析和主成分分析。结果:相似度结果显示,不同产地酸枣仁三维荧光图谱相似性均为1,相似性极高;聚类结果与主成分分析结果一致,明确将样品分为3类,均能通过区域积分值实现酸枣仁正品、伪品、掺伪品的区分。结论:利用三维荧光技术可以对不同产地酸枣仁质量进行一致性评价,实现酸枣仁真伪的快速鉴别,为酸枣仁的质量评价提供参考。

基于支持向量机的网格化电网负荷预测算法设计

针对电网负荷预测算法预测能力较差、效率偏低的问题,文中提出了一种PCA-PSO-SVM算法。其在经典粒子群算法的基础上引入主元分析法,使模型具有降低数据维度及算法冗余度的特性。同时通过改进的PCA-PSO算法对SVM模型的内置参数进行最优选取,从而使改进后的SVM模型具有最佳的分类性能。在实验测试中,采用PCA算法选取了91%贡献度内的6项数据特征进行样本数据训练。结果表明,相较于其他对比算法,该文算法预测结果的RMSE、MAE与MAPE误差值均为最小,证明其可对网格化电网负荷加以预测。此外,该算法还能提升传统算法的准确度,为电力负荷分配提供有力支持。

基于PCA和ICA模式融合的非高斯特征检测识别

针对无人船(Unmanned surface vehicle,USV)航行位姿观测数据的非高斯性/高斯性判别问题,提出一种基于主成分分析(Principal component analysis,PCA)和独立成分分析(Independent component analysis,ICA)模式融合的非高斯特征检测识别方法.首先,采用基于标准化加权平均和信息熵的数据预处理方法.其次,引入混合加权核函数并使用灰狼优化(Grey wolf optimization,GWO)算法进行参数优化,以提高PCA方法的准确性.同时,该算法采用一种新的非线性控制因子策略,提高全局和局部搜索能力.最后,建立了一种基于ICA和PCA联合的相关性分析方法来实现多维数据的降维,在降维数据的基础上综合T型多维偏度峰度检验法和KS(Kolmogorov-Smirnov)检验法进行非高斯性/高斯性特征检测识别.该方法考虑了非线性非高斯的噪声对降维结果精确度的影响,有效降低了多维数据非高斯检测的复杂度,同时也为后续在实际USV位姿估计等应用中提供了保障.实验表明,该方法具有较高的准确性和稳定性,可为USV航行位姿观测数据处理提供支持.

基于降维处理的快速EMT图像重建算法

电磁层析成像技术(EMT)具有非侵入、响应速度快、成本低等优点,在工业过程监测和多相流测量等领域有广泛的应用前景。该文针对电磁层析成像逆问题的病态性,提出了1种非迭代的、基于灵敏度矩阵降维的EMT图像重建算法,应用核主成分分析(KPCA)方法对灵敏度矩阵进行降维,有效降低了算法计算复杂度,同时降低了灵敏度矩阵的病态程度。为验证该算法的有效性,将该算法应用于平面EMT金属探伤,并将其与传统的线性反投影算法和Landweber迭代法进行比较。仿真和实验结果表明,该算法的成像质量远高于线性反投影算法,与Landweber迭代法相近,且该算法的计算耗时仅为Landweber迭代法的20%左右。