Face recognition using illuminant locality preserving projections

A novel supervised manifold learning method was proposed to realize high accuracy face recognition under varying illuminant conditions. The proposed method, named illuminant locality preserving projections （ILPP）, exploited illuminant directions to alleviate the effect of illumination variations on face recognition. The face images were first projected into low dimensional subspace, Then the ILPP translated the face images along specific direction to reduce lighting variations in the face. The ILPP reduced the distance between face images of the same class, while increase the dis tance between face images of different classes. This proposed method was derived from the locality preserving projections （LPP） methods, and was designed to handle face images with various illumi nations. It preserved the face image＇ s local structure in low dimensional subspace. The ILPP meth od was compared with LPP and discriminant locality preserving projections （DLPP）, based on the YaleB face database. Experimental results showed the effectiveness of the proposed algorithm on the face recognition with various illuminations.

SELF-DEPENDENT LOCALITY PRESERVING PROJECTION WITH TRANSFORMED SPACE-ORIENTED NEIGHBORHOOD GRAPH

Locality preserving projection （LPP） is a typical and popular dimensionality reduction （DR） method,and it can potentially find discriminative projection directions by preserving the local geometric structure in data. However,LPP is based on the neighborhood graph artificially constructed from the original data,and the performance of LPP relies on how well the nearest neighbor criterion work in the original space. To address this issue,a novel DR algorithm,called the self-dependent LPP （sdLPP） is proposed. And it is based on the fact that the nearest neighbor criterion usually achieves better performance in LPP transformed space than that in the original space. Firstly,LPP is performed based on the typical neighborhood graph; then,a new neighborhood graph is constructed in LPP transformed space and repeats LPP. Furthermore,a new criterion,called the improved Laplacian score,is developed as an empirical reference for the discriminative power and the iterative termination. Finally,the feasibility and the effectiveness of the method are verified by several publicly available UCI and face data sets with promising results.

Sparse Kernel Locality Preserving Projection and Its Application in Nonlinear Process Fault Detection

Locality preserving projection (LPP) is a newly emerging fault detection method which can discover local manifold structure of a data set to be analyzed, but its linear assumption may lead to monitoring performance degradation for complicated nonlinear industrial processes. In this paper, an improved LPP method, referred to as sparse kernel locality preserving projection (SKLPP) is proposed for nonlinear process fault detection. Based on the LPP model, kernel trick is applied to construct nonlinear kernel model. Furthermore, for reducing the computational complexity of kernel model, feature samples selection technique is adopted to make the kernel LPP model sparse. Lastly, two monitoring statistics of SKLPP model are built to detect process faults. Simulations on a continuous stirred tank reactor (CSTR) system show that SKLPP is more effective than LPP in terms of fault detection performance.

Fault Diagnosis Model Based on Feature Compression with Orthogonal Locality Preserving Projection

Based on feature compression with orthogonal locality preserving projection（OLPP）,a novel fault diagnosis model is proposed in this paper to achieve automation and high-precision of fault diagnosis of rotating machinery.With this model,the original vibration signals of training and test samples are first decomposed through the empirical mode decomposition（EMD）,and Shannon entropy is constructed to achieve high-dimensional eigenvectors.In order to replace the traditional feature extraction way which does the selection manually,OLPP is introduced to automatically compress the high-dimensional eigenvectors of training and test samples into the low-dimensional eigenvectors which have better discrimination.After that,the low-dimensional eigenvectors of training samples are input into Morlet wavelet support vector machine（MWSVM） and a trained MWSVM is obtained.Finally,the low-dimensional eigenvectors of test samples are input into the trained MWSVM to carry out fault diagnosis.To evaluate our proposed model,the experiment of fault diagnosis of deep groove ball bearings is made,and the experiment results indicate that the recognition accuracy rate of the proposed diagnosis model for outer race crack、inner race crack and ball crack is more than 90%.Compared to the existing approaches,the proposed diagnosis model combines the strengths of EMD in fault feature extraction,OLPP in feature compression and MWSVM in pattern recognition,and realizes the automation and high-precision of fault diagnosis.

Multimode Process Monitoring Based on Fuzzy C-means in Locality Preserving Projection Subspace

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy C-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

Supervised local and non-local structure preserving projections with application to just-in-time learning for adaptive soft sensor

In soft sensor field, just-in-time learning(JITL) is an effective approach to model nonlinear and time varying processes. However, most similarity criterions in JITL are computed in the input space only while ignoring important output information, which may lead to inaccurate construction of relevant sample set. To solve this problem, we propose a novel supervised feature extraction method suitable for the regression problem called supervised local and non-local structure preserving projections(SLNSPP), in which both input and output information can be easily and effectively incorporated through a newly defined similarity index. The SLNSPP can not only retain the virtue of locality preserving projections but also prevent faraway points from nearing after projection,which endues SLNSPP with powerful discriminating ability. Such two good properties of SLNSPP are desirable for JITL as they are expected to enhance the accuracy of similar sample selection. Consequently, we present a SLNSPP-JITL framework for developing adaptive soft sensor, including a sparse learning strategy to limit the scale and update the frequency of database. Finally, two case studies are conducted with benchmark datasets to evaluate the performance of the proposed schemes. The results demonstrate the effectiveness of LNSPP and SLNSPP.

Locality Preserving Discriminant Projection for Speaker Verification

In this paper, a manifold subspace learning algorithm based on locality preserving discriminant projection (LPDP) is used for speaker verification. LPDP can overcome the deficiency of the total variability factor analysis and locality preserving projection (LPP). LPDP can effectively use the speaker label information of speech data. Through optimization, LPDP can maintain the inherent manifold local structure of the speech data samples of the same speaker by reducing the distance between them. At the same time, LPDP can enhance the discriminability of the embedding space by expanding the distance between the speech data samples of different speakers. The proposed method is compared with LPP and total variability factor analysis on the NIST SRE 2010 telephone-telephone core condition. The experimental results indicate that the proposed LPDP can overcome the deficiency of LPP and total variability factor analysis and can further improve the system performance.

A Comparative Study of Locality Preserving Projection and Principle Component Analysis on Classification Performance Using Logistic Regression

There are a variety of classification techniques such as neural network, decision tree, support vector machine and logistic regression. The problem of dimensionality is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity, however, we need to use dimensionality reduction methods. These methods include principal component analysis (PCA) and locality preserving projection (LPP). In many real-world classification problems, the local structure is more important than the global structure and dimensionality reduction techniques ignore the local structure and preserve the global structure. The objectives is to compare PCA and LPP in terms of accuracy, to develop appropriate representations of complex data by reducing the dimensions of the data and to explain the importance of using LPP with logistic regression. The results of this paper find that the proposed LPP approach provides a better representation and high accuracy than the PCA approach.

基于KLPP-K-means-BiLSTM的台区短期电力负荷预测

随着智能电网的发展,各场景的用电更加多元化,而准确的台区负荷预测是确保相关电力部门制定合适检修任务的关键,同时为有序用电、电网经济运行提供重要参考。为了挖掘台区负荷的特征以提高台区负荷预测的精度,提出了一种基于核主元分析与局部保持投影降维、K均值聚类算法(K-means)以及双向长短时记忆网络(BiLSTM)的台区电力负荷预测方法。首先利用核局部保持投影(KLPP)对台区多特征负荷数据进行降维以提取主要特征信息;然后采取K-means聚类算法将相似特征的数据归类成各自的簇集;最后针对聚类后的各典型类型,有针对性地训练BiLSTM,并选取中国某高校低压台区负荷作为算例与其他经典预测方法进行对比分析,结果表明所提方法更拟合实际负荷走向,有效提升了预测效果。

基于自适应LPP特征降维和改进VPMCD的滚动轴承故障诊断

针对机械系统状态监测与故障诊断中存在的故障特征维数较高及模式识别导致的耗时较高问题,提出了一种基于自适应局部保持投影(Locality Preserving Projection,LPP)特征降维和改进多变量预测模型(Variable Predictive Model based Class Discriminate,VPMCD)的故障诊断方法。首先,从滚动轴承振动信号中提取时频域特征、能量特征,以及复杂度特征组成高维故障特征数据集;其次,利用自适应LPP方法对高维故障特征数据集进行降维处理,得到低维敏感故障特征;最后,采用改进VPMCD方法对低维敏感故障特征进行分类识别,进而判断故障类型。通过滚动轴承故障诊断试验分析表明,自适应LPP方法克服了传统LPP方法需要人工选取参数的缺陷,在获得低维敏感故障特征的基础上具有较少计算时间,相比主成分分析(Principal Component Analysis,PCA)、局部切空间排列(Local Tangent Space Alignment,LTSA)、线性局部切空间排列(Linear Local Tangent Space Alignment,LLTSA)、等距特征映射(Isometric Mapping,Isomap),以及局部线性嵌入(Locally Linear Embedding,LLE)等算法具有明显的优势;改进VPMCD方法可克服人工选择模型的偶然性和片面性,在滚动轴承10种故障状态的识别中获得了99.4%的诊断精度,相比优化参数支持向量机方法提高了故障诊断效率,大大降低了识别时间,具有一定的优越性。

基于RMDLPP的雷达空中目标分类

针对鉴别局部保持投影(discriminant locality preserving projections, DLPP)在窄带雷达目标数据降维时出现的类内离散度矩阵奇异和对孤立点敏感进而导致类别之间可分性弱的问题,提出了基于鲁棒性边界DLPP(robust margin DLPP, RMDLPP)的窄带雷达空中目标分类方法。首先,在计算样本之间距离时将两样本点的欧氏距离与同类样本均值相关联。然后,挑选一定数量的边界样本点进行处理并对优化DLPP目标函数进行降维。最后,使用高性能分类器对降维后的数据进行区分,实现对空中目标的分类。通过对X波段对空警戒雷达实测数据的对比实验表明,所提方法具有更好的分类准确率和对孤立点的鲁棒性。

KLPP特征约简与RELM的高压隔膜泵单向阀故障诊断

为此提出基于核局部保持投影(KLPP)和正则化极限学习机(RELM)的高压隔膜泵单向阀故障诊断方法。首先,提取单向阀振动信号的时域、频域、时频域特征,构建多域特征集;然后,通过KLPP算法对构建的多域特征集进行维数约简;最后,建立基于RELM的故障诊断模型,用于识别单向阀运行状态。实验结果表明,基于多域特征的故障诊断方法检测精度高于单域特征识别方法;KLPP约简多域特征集,可以有效消除信息冗余;建立的RELM故障诊断模型识别精度达到98.89%,能够有效识别高压隔膜泵单向阀故障类型。

多信息融合的LPP算法

针对原始局部保持投影(LPP:Local Preserving Projection)算法难以准确获取非均匀高维数据的局部流形结构且未利用样本类别信息的缺陷,提出一种多信息融合的局部保持投影算法(MIF-LPP:Multi-Information Fusion Local Preserving Projection)。该算法使用改进后的标准欧氏距离获取样本的近邻和互邻信息,降低了样本点分布不均和不同维度数据量纲差异的影响。通过融合样本的类别信息构造权值矩阵,进而获得数据的低维本质流形。最后,分别在CWRU(Case Western Reserve University)数据集和本实验室轴承数据集上验证该算法的有效性。实验结果表明,MIF-LPP算法的特征提取性能明显优于其他算法,并且对邻域值具有鲁棒性。

Surface Detection of Continuous Casting Slabs Based on Curvelet Transform and Kernel Locality Preserving Projections

Longitudinal cracks are common defects of continuous casting slabs and may lead to serious quality accidents. Image capturing and recognition of hot slabs is an effective way for on-line detection of cracks, and recognition of cracks is essential because the surface of hot slabs is very complicated. In order to detect the surface longitudinal cracks of the slabs, a new feature extraction method based on Curvelet transform and kernel locality preserving projections （KLPP） is proposed. First, sample images are decomposed into three levels by Curvelet transform. Second, Fourier transform is applied to all sub-band images and the Fourier amplitude spectrum of each sub-band is computed to get features with translational invariance. Third, five kinds of statistical features of the Fourier amplitude spectrum are computed and combined in different forms. Then, KLPP is employed for dimensionality reduction of the obtained 62 types of high-dimensional combined features. Finally, a support vector machine （SVM） is used for sample set classification. Experiments with samples from a real production line of continuous casting slabs show that the algorithm is effective to detect longitudinal cracks, and the classification rate is 91.89%.

基于LPP特征空间重构的故障检测策略

针对多模态工业过程数据中存在方差差异显著的问题,提出了一种基于LPP特征空间重构的故障检测策略.首先,采用局部保持投影对过程数据进行降维处理,去除数据的冗余信息和噪声,降低计算复杂度;其次,将变分高斯混合模型应用于过程数据,确定操作模式的数量,并对每种模式下的数据进行聚类;再次,将每种模式下的数据利用所属模式的信息进行标准化处理,去除数据的多模态特征;最后,使用统计量T^(2)对过程进行监控.通过一个多模态数值例子和半导体蚀刻过程验证了所提方法的有效性.

Full-viewpoint 3D Space Object Recognition Based on Kernel Locality Preserving Projections

Space object recognition plays an important role in spatial exploitation and surveillance, followed by two main problems： lacking of data and drastic changes in viewpoints. In this article, firstly, we build a three-dimensional （3D） satellites dataset named BUAA Satellite Image Dataset （BUAA-SID 1.0） to supply data for 3D space object research. Then, based on the dataset, we propose to recognize full-viewpoint 3D space objects based on kernel locality preserving projections （KLPP）. To obtain more accurate and separable description of the objects, firstly, we build feature vectors employing moment invariants, Fourier descriptors, region covariance and histogram of oriented gradients. Then, we map the features into kernel space followed by dimensionality reduction using KLPP to obtain the submanifold of the features. At last, k-nearest neighbor （kNN） is used to accomplish the classification. Experimental results show that the proposed approach is more appropriate for space object recognition mainly considering changes of viewpoints. Encouraging recognition rate could be obtained based on images in BUAA-SID 1.0, and the highest recognition result could achieve 95.87%.

基于DLPP-LOF的信息物理系统异常诊断方法

为了准确诊断信息物理系统的异常类型,提出了一种新的基于动态局部保持投影-局部离群因子(dynamic locality preserving projections-local outlier factor,DLPP-LOF)的方法。首先,采用数据增广策略在判别模型中考虑自相关性,进而利用对数据分布没有要求的流形学习方法——局部保持投影(locality preserving projections,LPP)提取特征。其次,计算测试数据特征相对于训练数据集各类别特征的局部离群因子(local outlier factor,LOF),将具有最小离群因子的类作为测试数据的类别。确定了异常类别后,在已建立的历史异常数据及相应决策方案库中搜索制定应急响应预案。最后,将所提出的DLPP-LOF方法在典型信息物理系统上进行测试,验证了其有效性及优越性。

3D Face Reconstruction from a Single Image Using a Combined PCA-LPP Method

In this paper, we proposed a combined PCA-LPP algorithm toimprove 3D face reconstruction performance. Principal component analysis(PCA) is commonly used to compress images and extract features. Onedisadvantage of PCA is local feature loss. To address this, various studies haveproposed combining a PCA-LPP-based algorithm with a locality preservingprojection (LPP). However, the existing PCA-LPP method is unsuitable for3D face reconstruction because it focuses on data classification and clustering.In the existing PCA-LPP, the adjacency graph, which primarily shows the connectionrelationships between data, is composed of the e-or k-nearest neighbortechniques. By contrast, in this study, complex and detailed parts, such aswrinkles around the eyes and mouth, can be reconstructed by composing thetopology of the 3D face model as an adjacency graph and extracting localfeatures from the connection relationship between the 3D model vertices.Experiments verified the effectiveness of the proposed method. When theproposed method was applied to the 3D face reconstruction evaluation set,a performance improvement of 10% to 20% was observed compared with theexisting PCA-based method.

基于可变滑动窗口KLPP的故障检测

为了提高KLPP在故障检测过程中对非线性和时变特性的自适应能力,提出一种基于可变滑动窗口KLPP(VMWKLPP)的故障检测方法。利用训练数据建立KLPP模型,并计算其统计量和控制限;对测试样本块进行检验,通过正常过程的变化来调节窗口的大小,选择最优的窗口大小。滑动窗口来添加新的样本块和丢弃旧的样本块,实现窗口数据样本的实时更新,以进一步更新KLPP模型和控制限。将该方法运用于田纳西-伊斯曼过程中,仿真结果表明,与KLPP和滑动窗口KLPP(MWKLPP)相比,VMWKLPP方法在工业过程监控中具有明显的优越性。

基于偶极子成像和3D卷积神经网络的源域运动想象解码方法

目的为充分保留和利用运动想象(motor imagery,MI)时偶极子的时空信息,本文提出一种新的偶极子成像(dipoles imaging,DI)结合3维卷积神经网络(3D convolutional neural network,3DCNN)的源域MI解码方法(DI-3DCNN)。方法首先,基于脑源成像(electroencephalography source imaging,ESI)技术计算运动想象脑电信号的偶极子源估计;接着,获取每类MI任务的平均偶极子源估计,基于数据驱动自动选择每类任务中偶极子激活水平较高且最大区分于其他任务的时刻作为中心采样点,再对中心采样点进行前后延伸并按任务顺序组合,形成感兴趣时间(time of interest,TOI);其次,选择覆盖高激活偶极子的Desikan-Killiany(DK)神经分区,并对局部保持投影方法(local preserving projection,LPP)增加DK分区约束,获得一种改进的有监督LPP(LPP DK);进而,基于LPP DK分别将所选择左、右半脑分区内的偶极子坐标从3维(three dimensional,3D)降成2维,获得具有神经生理先验信息的偶极子2D坐标,再结合TOI内各采样点处偶极子的幅值信息进行成像,并进行插值、下采样操作,得到偶极子的2D幅值图;随后,将TOI内偶极子的2D幅值图按时间顺序堆叠,获得左、右半脑的3D偶极子特征图,并将其作为网络的输入数据;最后,根据输入数据的特点,设计一种双分支3D卷积神经网络(dual-branched 3DCNN,DB3DCNN)实现MI解码。结果基于BCI competition IV 2a数据集进行实验研究,取得了86.50%的平均解码准确率。结论基于DI所得3D偶极子特征图能够较好地保留偶极子的最佳激活时间、程度及生理空间信息,且与DB3DCNN性能匹配。