基于独立成分分析的掌纹识别

本文研究了独立成分分析(ICA)两种不同的结构ICA I和ICAII在掌纹识别中的应用.为了提高识别准确性和可靠性,该方法首先对掌纹图像进行预处理,提取掌纹感兴趣(ROI)区域进行特征提取和匹配.为了减少计算量,运用ICA算法之前,先采用主成分分析(PCA)算法去除掌纹图像的二阶统计特征相关性,其余的高阶统计特征由ICA分离.对于PolyU掌纹图像库,基于ICA模型的预测误差平方和(SPE)小于PCA,而且重构的原始图像优于PCA.为了比较两种算法识别性能,本丈分别用PCA、ICA I、ICAII提取特征掌纹子空间,然后将待识别图像投影到低维子空间上,最后用余弦距离进行掌纹匹配.实验结果表明,ICA算法两种结构的识别率均高于PCA,ICAII在性能上优于ICA I.     

ICA的近红外光谱分析软件的研制

研制了基于独立分量分析方法的近红外光谱分析软件.该软件包括光谱解析、光谱建模和未知成分含量测定三个模块,使用了小波分析、ICA和BP神经网络等数据处理方法.将这种软件用于实测的玉米近红外光谱分析,所得结果令人满意.使用LabVIEW与MATLAB软件混合编程,充分利用了各软件的优点,不仅程序简单,而且界面友好.     

基于独立成分分析的掌纹识别

本文研究了独立成分分析（ICA）两种不同的结构ICAⅠ和ICAⅡ在掌纹识别中的应用。为了提高识别准确性和可靠性,该方法首先对掌纹图像进行预处理,提取掌纹感兴趣（ROI）区域进行特征提取和匹配。为了减少计算量,运用ICA算法之前．先采用主成分分析（PCA）算法去除掌纹图像的二阶统计特征相关性,其余的高阶统计特征由ICA分离。对于PolyU掌纹图像库,基于ICA模型的预测误差平方和（SPE）小于PCA,而且重构的原始图像优于PCA。为了比较两种算法识别性能,本文分别用PCA、ICAⅠ、ICAⅡ提取特征掌纹子空间,然后将待识别图像投影到低维子空间上,最后用余弦距离进行掌纹匹配。实验结果表明,ICA算法两种结构的识别率均高于PCA,ICAⅡ在性能上优于ICAⅠ。     

近红外快速检测中的独立分量和遗传神经网络建模方法

采用独立分量分析方法提取近红外光谱的独立分量和影响矩阵,再用GA-BP神经网络对影响矩阵和浓度矩阵进行建模,提出了基于独立分量-遗传算法-人工神经网络回归的近红外光谱建模方法.分析了独立分量数和网络中间隐层的神经元数对模型性能的影响.采用该方法对小麦样品中的水分、蛋白质、淀粉3种主要成分含量进行测定,水分、蛋白质和淀粉的预测值和参考值之间的相关系数R分别为0.9670、 0.9804、0.9674.     

基于两被联件振动信号概率密度和PCA的螺栓松动识别方法研究

螺栓松动是一种常见且具有潜在危害的机械故障。考虑到螺栓松动会导致被联接件结合部动力参数发生变化,提出了一种基于两被联接件振动信号的松动识别方法。所提方法首先计算两信号的概率密度,并对概率密度曲线进行网格化处理生成概率矩阵,继而对概率矩阵进行主元分析(PCA),在合并两路信号经主元分析后所得投影矩阵之后,再次进行主元分析和投影。设计了两种识别方式,方式1首先按上述过程进行已知样本训练以得到各松动状态投影点,识别时根据所得投影点与各状态投影点间的欧式距离进行判断;方式2使用螺栓紧固状态时所得样本数据和现场实测数据直接按上述过程进行计算,并根据PCA特性设计了松动判别条件。试验验证表明所提方法能够准确区分不同松动状态,且识别方式2操作简便,无需故障样本,易于实际应用。     

PCA与SVD信号处理效果相似性与机理分析

将主成分分析(Principal Component Analysis,PCA)用于信号处理,并与奇异值分解(Singular Value Decomposition,SVD)方法比较。分析总结PCA及SVD信号处理原理,提出基于PCA的特征值差分谱理论用于信号消噪。结果表明,PCA与SVD的处理效果较相似,相似性原因为原始矩阵右奇异向量即为协方差矩阵特征向量。SVD较PCA的重构误差小,因SVD无需计算协方差矩阵,可避免舍入误差产生。     

张量主成分分析与高维信息压缩方法

本文概述了信息压缩背景下的张量主成分分析的研究历史与发展现状,并展望了一些可能的研究领域.首先,我们回顾了张量以及张量分解的历史,在信息压缩背景下张量分解可以统一表达为一个普适的统计模型;其次,按经典主成分分析(PCA)、稳健主成分分析以及稀疏主成分分析三大类,我们详述了每类在多样本和单样本情形下的统计理论和优化算法的进展,其中又由简单数据结构到复杂数据结构依次对向量数据、矩阵数据以及张量数据的PCA发展进行了概述.     

基于GA-BP神经网络的彩色扫描仪光谱特征化

为了实现彩色扫描仪的光谱特征化,采用一种GA修正的BP神经网络与PCA相结合的方法对其进行研究。首先,通过主成分分析,对训练样本的光谱反射率进行降维,以RGB信号和降维后的光谱数据作为输入、输出变量进行GA-BP神经网络的建模,对任意RGB信号都可以通过模型得到其低维光谱信号;再通过主成分分析重构光谱反射率,由此实现RGB信号对光谱反射率的重构,即实现扫描仪的光谱特征化。实验结果表明,GA的优化有效地改善了BP神经网络的极值问题,提高了模型的预测精度,PCA在不影响模型精度的同时提高了模型的效率。由此说明,所提出的模型能够满足扫描仪光谱特征化的需求。     

近红外光谱小波投影寻踪回归定量分析方法研究

为改进近红外光谱结构特征与定量回归模型的非线性拟合度和充分利用光谱中的非线性特征,提出了一种光谱小波投影寻踪定量分析方法。该方法对光谱进行小波分解后,用高斯混合模型噪声估计法降噪,对降噪后的小波系数向最优投影方向降维,用多项式岭函数拟合定量回归关系。建立黄酒近红外光谱快速预测酒精度小波投影寻踪回归模型,其相关系数R2和交叉检验标准差RMSECV分别为0.957和0.37838,该法比分析多元线性回归和偏最小二乘回归定量分析2种常规定量分析方法具有更优的预测效果,能更为有效地应用于近红外光谱快速定量分析检测。     

一种基于LMS加权的残差补偿光谱降维模型研究

目的在PCA算法的基础上提出一种基于LMS锥响应加权的残差补偿光谱降维模型。方法介绍以LMS为加权函数对源光谱加权以及用残差光谱对模型补偿的基本框架。以Munsell色卡作为训练样本,以多光谱图像和SG色卡为检测样本,用文中算法与主成分分析算法分别对其进行降维、重构。结果在不同维数下,采用文中算法重构都具有较高的色度精度,该算法有效提高了主成分分析算法的色度精度,且在变光源情况下仍具有较高的色度稳定性。结论该降维算法采用LMS加权并对残差光谱补偿是一种精度较高的光谱降维模型。     

Multiscale ICA for fundus image analysis

Independent component analysis is a technique used for separation of statistically independent sources. It can estimate unknown sources from a mixture of sources without any prior knowledge about them. The sources should be non‐Gaussian and independent with each other. In this work, multiscale ICA is proposed for medical images (fundus images, MRI Images). The data matrix is formed by considering the higher sub‐bands of multiscale decompositions. Performance of multiscale ICA is evaluated and compared with the ICA algorithms using simulated signals and different medical images using Amari performance index and Comon test values. Results show that API and Comon test values are less for multiscale ICA for simulated signals. In case of pathological images, the features are separated correctly by multiscale ICA. Multiscale ICA performs better than simple ICA for separation and detection of independent components from medical images (fundus images), such as blood vessels and artifacts. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 327–337, 2013     

State-space independent component analysis for nonlinear dynamic process monitoring

The cost effective benefits of process monitoring will never be over emphasised. Amongst monitoring techniques, the Independent Component Analysis (ICA) is an efficient tool to reveal hidden factors from process measurements, which follow non-Gaussian distributions. Conventionally, most ICA algorithms adopt the Principal Component Analysis (PCA) as a pre-processing tool for dimension reduction and de-correlation before extracting the independent components (ICs). However, due to the static nature of the PCA, such algorithms are not suitable for dynamic process monitoring. The dynamic extension of the ICA (DICA), similar to the dynamic PCA, is able to deal with dynamic processes, however unsatisfactorily. On the other hand, the Canonical Variate Analysis(CVA) is an ideal tool for dynamic process monitoring, however is not sufficient for nonlinear systems where most measurements follow non-Gaussian distributions. To improve the performance of nonlinear dynamic process monitoring, a state space based ICA (SSICA) approach is proposed in this work. Unlike the conventional ICA, the proposed algorithm employs the CVA as a dimension reduction tool to construct a state space, from where statistically independent components are extracted for process monitoring. The proposed SSICA is applied to the Tennessee Eastman Process Plant as a case study. It shows that the new SSICA provides better monitoring performance and detect some faults earlier than other approaches, such as the DICA and the CVA.     

Analysis of unsupervised learning techniques for face recognition

Face recognition has always been a potential research area because of its demand for reliable identification of a human being especially in government and commercial sectors, such as security systems, criminal identification, border control, etc. where a large number of people interact with each other and/or with the system. The last two decades have witnessed many supervised and unsupervised learning techniques proposed by different researchers for the face recognition system. Principal component analysis (PCA), self‐organizing map (SOM), and independent component analysis (ICA) are the most widely used unsupervised learning techniques reported by research community. This article presents an analysis and comparison of these techniques. The article also includes two SOM processing methods global SOM (GSOM) and local SOM (LSOM) for performance evaluation along with PCA and ICA. We have used two different databases for our analysis. The simulation result establishes the supremacy of GSOM in general among all the unsupervised techniques. © 2010 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 20, 261–267, 2010     

Independent Component Analysis–Based Feature Extraction Technique for Defect Classification Applied for Pulsed Eddy Current NDE

With the development of nondestructive detection, the emerging testing techniques provide new challenges to signal analysis and interpretation approach applied to the inspection evaluation. Some researchers have developed the methods that focus on feature analysis of detected signals. This article presents a new feature analysis by the Independent Component Analysis (ICA) approach to evaluate the defects tested by the pulsed eddy current (PEC) technique. ICA is a high-order statistics technique used to separate multi-unknown sources, which has been successfully applied to facial image identification and separation of the components of 1D signal. In this article, the ICA approach is utilized to project the response signals of various defects into the independent components (ICs) feature subspace by signal representation model. Dependent on the selected ICs, each defect is represented by different projected coefficients, which are proposed to discriminate and classify the defects that belong to three categories. The improved ICA model is proposed to improve the classification of two similar categories of single defects: metal loss and subsurface defects. The evaluation using the series of experimental data has validated the classification of single defects and the defects with lift-off effect by our ICA approach. The comparison with Principal component analysis (PCA)–based approach further verified the better performance of the ICA-based model.     

Comparative analyses of anthropometry associated with overweight and obesity: PCA and ICA approaches

This study undertakes to explore the co-varying structure in anthropometric variables that might be affected by the recent surge of overweight and obesity. The increase of overweight and obesity makes the distribution of body dimensions asymmetric by the long tail in distribution (skewness, kurtosis). Principal component analysis (PCA) has been well applied to understand the co-varying body dimensions. However, because PCA decomposes covariance/correlation matrix, the effects of overweight and obesity may not be well captured. Independent component analysis (ICA) is a variant of PCA with the additional assumptions of components being non-Gaussian and independent, in which kurtosis is decomposed. PCA and ICA are applied on the anthropometric data from the North American portion of the Civilian American and European Surface Anthropometry Resource (CAESAR) project. ICA yields more interpretable results by visual inspection than corresponding PCA results. The first independent component (IC 1) is associated with hip/thigh circumferences and chest/waist circumferences and has the largest correlation coefficients with body mass index (BMI). Only the second IC shows the overall size factor that reveals gender difference while principal components 1, 2 and 3 show gender difference. The ICs 3 (torso length) and 4 (arm and leg lengths) are associated with individual differences in body dimensions. The ranges of 38 body dimensions are identified in order to satisfactorily meet the anthropometric variations for both males and females. The ICA gives promise of becoming a valuable tool in the field of ergonomics.     

基于独立分量分析的六维力传感器标定矩阵的校正

为了提高六维力传感器的测量精度,提出了一种基于独立分量分析(ICA)的静态标定方法。作用在上平台的各维力经过六维力传感器后形成混合信号,该方法先对混合信号进行ICA分解,即将其分离成独立的分量,再结合理想源信号恢复出各维真实作用力的排序和幅值,最后得出校正后的标定矩阵。仿真结果表明,校正后的标定矩阵更接近标准的标定矩阵,从而提高了六维力传感器的测量精度。     

温变工况下超定独立成分分析导波监测方法

基于超声导波的结构健康监测技术在实际工程应用中受到变化的环境工况条件的影响,由于独立成分分析方法对于处理时变工况条件下的大量监测导波信号存在局限性,以及对不同程度的损伤表征研究存在不足,提出一种基于超定独立成分分析的导波监测方法并改进了基于k均值聚类的损伤指标。以广泛存在的环境温度作为环境变量,通过主成分分析从大量导波信号组成的观测矩阵中确定独立分量个数,使用独立成分分析将处理后的导波信号分解为独立分量,能够有效地将损伤与环境工况的影响分离到不同的独立分量中。对长期经受环境温度变化的铝板进行了导波监测实验,结果表明该方法能够有效减少独立分量数目并从大量导波信号中排除环境温度的干扰识别出损伤,并且对处于温变条件下的完整和不同损伤程度的铝板进行了损伤识别实验,进一步研究了该方法在排除环境温度干扰的同时表征损伤程度的效果。