基于双向二维主成分分析的运动目标跟踪

为克服二维主成分分析（2DPCA）跟踪效率低的缺点,提出一种基于双向二维主成分分析（Bi-2DPCA）的运动目标跟踪算法。采用双向二维主成分分析作为目标表示的方法建立目标图像子空间,同时在图像均值与协方差矩阵的更新中引入基于目标图像匹配程度的自适应增量因子的增量学习的方法进一步提高算法效率。在多个包含动态背景的图像序列上的对比实验结果表明算法能在目标处于部分遮挡的情况下准确跟踪目标,同时算法在效率上高于基于二维主成分分析的目标跟踪算法。     

基于对称的二维主成分分析及其在人脸识别中的应用

在二维主成分分析算法中引入了对称性思想,提出了基于对称的二维主成分分析算法（STDPCA）。在该算法中,首先把人脸图像分解成奇对称图像和偶对称图像,然后分别在这两类图像中进行二维主成分分析,提取所需要的特征。该算法不仅有效利用了二维主成分分析算法的优点,而且也考虑了人脸对称性的特点,因此在人脸识别中有较高的识别率。在著名人脸库ORL、YALE中的实验证明了该算法的有效性。     

基于完全二维对称主成分分析的人脸识别

镜像对称性是人脸的一个直观明显的自然特性,结合该特性在完全二维主成分分析的基础上提出完全二维对称主成分分析的人脸识别方法。该方法通过镜像变换得到奇对称样本和偶对称样本,分别对奇偶对称样本进行完全二维主成分分析,通过奇偶加权因子对奇偶对称样本的特征矩阵进行组合,并采用最近邻距离分类器分类。在ORL人脸数据库上的实验表明,该方法有较好的识别效果。     

基于双向二维主成分分析的掌纹识别

掌纹识别是一门新兴的生物特征识别技术.使用主成分分析对图像向量进行处理,向量维教一般都很高.二维主成分分析是直接采用二维图像矩阵来构建方差矩阵,与一维主成分分析相比能更精确地计算原始数据的协方差矩阵,双向二维主成分分析是二维主成分分析的改进算法,将其应用于掌纹识别,通过在水平和垂直2个方向上各执行1次二维主成分分析运算,消除了掌纹图像行和列的相关性,运用新准则选取了更适合于分类的主分量,大大压缩了特征的维数.在香港Poly-technic Universitv的Palmprint Database测试结果表明,该方法具有更高的识别率和更低的计算复杂度.     

最小化相关性的二维主成分分析

指出在二维主成分分析中,特征向量的任意两个分量之间是相关的,并给出此相关性的数学表达,进一步提出最小化相关性的二维主成分分析。该方法改进二维主成分分析的目标函数,最大化特征向量间总体散度的同时,最小化特征向量各分量间的相关性。最后,在Yale标准人脸库上的实验结果表明,文中方法有较强的特征抽取能力,在识别性能上优于二维主成分分析及对角二维主成分分析。     

改进的完全二维主成分分析及其在步态识别中的应用研究*

对完全二维主成分分析算法进行改进,提出三种不同的加权策略,详细地分析它们的本质,并将其应用到步态识别中。在中科院自动化所提供的CASIA（B）步态数据库下验证加权方案的有效性,实验结果表明加权幂指数的选取对识别结果的影响比较大,通过实验可以选取最佳的权值,能够做到提高识别性能。最后针对各个行走状态下的步态,分析了背包步态识别率低的原因。     

基于主成分分析法的多传感器数据融合算法研究

根据数理统计中的多元统计理论,用多个传感器对某一特性指标进行检测,将检测数据作为统计对象,提出了一种基于主成分分析的数据融合方法。该方法把各传感器的检测数据作为一个整体,定义总体的各主成分。通过计算测量值与主成分的复相关系数,给出了各传感器的综合支持程度和数据融合公式。实验表明,此方法比均值融合法更准确、有效。     

基于分块双向二维主成分分析的步态识别

提出了一种基于步态能量图和分块双向二维主成分分析进行步态特征的算法。首先对图像序列预处理提取运动轮廓,通过分析区域分布直方图检测出运动周期,生成步态能量图描述步态的空间和时间特性,继而使用分块双向二维主成分提取步态特征用以分类,最后在USF步态数据库上测试,并与其它几个算法进行比较。实验结果显示,该方法有更高的识别率和更低的计算复杂度。     

基于二维主成分分析的指纹识别算法

提出一种基于指纹统计特性的识别算法。该算法根据奇异点的位置和方向,提取指纹图像的感兴趣区域(ROI),并使用二维主成分分析(2DPCA)的方法进行统计特征的提取和识别。在FVC2002指纹数据库上进行实验,结果表明：相对于PCA,该方法的计算速度更快。相对于传统的基于特征点的方法,该方法的实现更为简便。     

基于二维主成分分析的指纹识别算法

提出一种基于指纹统计特性的识别算法.该算法根据奇异点的位置和方向,提取指纹图像的感兴趣区域(ROI),并使用二维主成分分析(2DPCA)的方法进行统计特征的提取和识别.在FVC2002指纹数据库上进行实验,结果表明:相对于PCA,该方法的计算速度更快.相对于传统的基于特征点的方法,该方法的实现更为简便.     

基于ICA-PCA的化工流程仪表故障诊断

传统的多元统计过程控制(MSPC)的故障诊断方法要求观测变量数据服从高斯分布,然而实际化工流程中的仪表数据中难以满足这一要求。针对这一问题,提出在仪表数据中提取分离出非高斯信息和高斯信息,并分别利用独立元分析法和主元分析法建立不同的故障诊断模型。在检测到发生故障后,通过改进的贡献度算法定位出发生故障的仪表。通过对Tennessee Eastman(TE)过程数据进行仿真研究,验证了ICA-PCA故障诊断法在化工流程仪表不同故障诊断中的有效性。     

Robust sparse principal component analysis

The model for improving the robustness of sparse principal component analysis（PCA） is proposed in this paper. Instead of the l2-norm variance utilized in the conventional sparse PCA model,the proposed model maximizes the l1-norm variance,which is less sensitive to noise and outlier. To ensure sparsity,lp-norm（0 p 1） constraint,which is more general and effective than l1-norm,is considered. A simple yet efficient algorithm is developed against the proposed model. The complexity of the algorithm approximately linearly increases with both of the size and the dimensionality of the given data,which is comparable to or better than the current sparse PCA methods. The proposed algorithm is also proved to converge to a reasonable local optimum of the model. The efficiency and robustness of the algorithm is verified by a series of experiments on both synthetic and digit number image data.     

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.     

基于随机矩阵变换的快速PCA算法

主成分分析PCA（PrincipleComponentAnalysis）是一种重要的分析方法,广泛应用于图像检索、机器学习、模式识别等领域。随着近年来数据维数越来越大,算法的稳定性、时间复杂度和内存使用成了PCA进一步应用所必须要解决的问题。为此提出一种快速算法,该算法利用随机矩阵构造卷数据降维矩阵,在保持点与点之间“核距离”不变的情况下,将待分解矩阵变换成一个低维矩阵。在没有偏差的情况下,将对原始大矩阵的分解变成对这个低维矩阵的分解,大幅降低了时间复杂度,减少了对内存的使用,同时增加了算法的稳定性,从而在根本上解决了上述3个问题。     

Asynchronism-based principal component analysis for time series data mining

Principal component analysis (PCA) is often applied to dimensionality reduction for time series data mining. However, the principle of PCA is based on the synchronous covariance, which is not very effective in some cases. In this paper, an asynchronism-based principal component analysis (APCA) is proposed to reduce the dimensionality of univariate time series. In the process of APCA, an asynchronous method based on dynamic time warping (DTW) is developed to obtain the interpolated time series which derive from the original ones. The correlation coefficient or covariance between the interpolated time series represents the correlation between the original ones. In this way, a novel and valid principal component analysis based on the asynchronous covariance is achieved to reduce the dimensionality. The results of several experiments demonstrate that the proposed approach APCA outperforms PCA for dimensionality reduction in the field of time series data mining.     

PCR法对杯芳烃分析体系中同系物的同时测定研究

杯芳烃是重要的超分子试剂,其同系物的吸收光谱重叠严重,难以直接同时测定.本文应用主成分回归法研究了对叔丁基杯[4]芳烃、对叔丁基杯[6]芳烃和对叔丁基杯[8]芳烃三种大分子同系化合物组成的混合体系中各组分同时直接测定,测定结果回收率为94%-111%,具有较高的准确度,且过程简单,运算快速,为同系物混合体系的同时测定研究提供了新的途径.     

Two-stage image denoising by principal component analysis with local pixel grouping

This paper presents an efficient image denoising scheme by using principal component analysis (PCA) with local pixel grouping (LPG). For a better preservation of image local structures, a pixel and its nearest neighbors are modeled as a vector variable, whose training samples are selected from the local window by using block matching based LPG. Such an LPG procedure guarantees that only the sample blocks with similar contents are used in the local statistics calculation for PCA transform estimation, so that the image local features can be well preserved after coefficient shrinkage in the PCA domain to remove the noise. The LPG-PCA denoising procedure is iterated one more time to further improve the denoising performance, and the noise level is adaptively adjusted in the second stage. Experimental results on benchmark test images demonstrate that the LPG-PCA method achieves very competitive denoising performance, especially in image fine structure preservation, compared with state-of-the-art denoising algorithms.     

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.     

多尺度主元分析的一种改进方法

MSPCA方法在生产过程监控方面有着广泛应用.本文在研究该方法的基础之上,提出了一些改进,在其进行小波分解后即对其小波系数进行阈值处理,使小波消噪与MSPCA方法合为一体,并运用统计控制图中的平方预测误差(SPE)图方法检测引起过程变化或故障的过程变量.在保证其MSPCA算法复杂度不变的前提下,能够消除数据的噪声污染,使故障诊断的误报大为减少.经检验,该算法确实可行,相对于小波消噪与MSPCA方法分别进行,效率提高了大约13%-17%.     

A fast pixel mapping algorithm using principal component analysis

Pixel mapping is one of the basic processes in color quantization. In this paper, we shall propose a new algorithm using principal component analysis as a faster approach to pixel mapping. Within much shorter search time, our new scheme can find the nearest color which is identical to the one found using a full search. The idea behind the proposed method is quite simple. First, we compute two principal component directions (PCDs) for the palette. Then, the projected values on PCDs are computed for each color in palette. Finally, the projected values, following the triangular inequality principle, can help us reduce the computation time for finding the nearest color. The experimental results reveal that the proposed scheme is more efficient than the previous work.