基于伪标签半监督核局部Fisher判别分析轴承故障诊断

针对轴承故障诊断应用中多特征融合导致的维度高、相关性强、信息冗余等问题,提出一种基于伪标签半监督核局部Fisher判别分析(Semi-supervised Kernel Local Fisher Discriminant Analysis, SS-KLFDA)轴承故障诊断方法。为了能利用大量无标签样本提高算法判别性能,该方法首先采用密度峰值聚类算法对样本进行聚类分析得到伪标签,然后通过增加规范化项到局部FDA算法的类内散度矩阵和类间散度矩阵中,以此保持无标签样本的聚类结构一致性,最后通过局部FDA算法来保持有标签样本类间散度最大化和类内散度最小化并求解最佳投影向量;为了能适应非线性数据降维,进一步给出了基于核的伪标签半监督局部Fisher判别算法。试验部分通过同其他流行降维算法在不同维度、不同特征集合以及不同分类算法条件下进行轴承故障诊断性能对比,结果表明基于伪标签半监督核局部Fisher判别分析方法的分类精度明显优于其他降维算法,投影后的系数向量具有更好的区分能力,使故障诊断性能得到了很大提升。     

直接无监督正交局部保持特征提取算法

基于局部保持投影发展出的一系列特征提取算法,在应用于人脸识别等高维小样本问题时,均需先采用PCA算法对高维样本降维后才能应用,故此以无监督鉴别分析算法为理论基础,提出了一种直接无监督正交局部保持算法。该算法利用拉普拉斯矩阵的性质进行相应的矩阵分解,可直接从高维样本的原始空间中提取投影矩阵,因而无需先采用PCA降维处理,且解决了无监督鉴别分析算法的小样本问题。为了进一步提高算法的识别性能,给出了基于QR分解的正交投影矩阵的求解方法。人脸库和掌纹库上的实验结果表明了所提算法的有效性。     

基于Log-Gabor小波和正交UDP的人脸识别

针对人脸识别中特征提取的小样本问题,对原始的非监督判别映射(UDP)算法进行了改进,提出一种基于Log-Gabor和正交非监督判别映射(Orthogonal UDP)的人脸识别算法——LGOUDP算法.此算法首先采用Log-Gabor小波对图像进行滤波来提取高阶统计信息,然后提出最大化非局部散度和局部散度的权值差和加入...     

模拟退火与模糊C-均值聚类相结合的图像分割算法

模糊C-均值(FCM)聚类算法是一种结合无监督聚类和模糊集合概念的图像分割技术,比较有效,但存在着受初始聚类中心和隶属度矩阵影响,可能收敛到局部极小的缺点.将模拟退火算法(SA)与模糊C-均值聚类算法相结合,在合理选择冷却进度表的基础上,依据模糊C-均值聚类算法建立模拟退火算法的目标函数,实现了基于模拟退火的模糊C-均值聚类图像分割算法.实验表明,该方法具有搜索全局最优解的能力,因而可得到很好的图像分割结果.     

人脸识别的一种新的特征提取方法

针对人脸识别中判别特征的提取问题,本文提出了一种新的人脸识别算法—扩展保局投影（ELPP）。普通保局投影（LPP）在构建权图时侧重保持样本的局部结构,属于无监督学习算法。扩展保局投影在保局投影的基础上进行扩展,通过引入可调因子,在保持人脸图像局部流形结构的同时考虑样本的类别信息,从而充分提取样本的判别特征。本文采用最小近邻分类器估算识别率。在Yale人脸库以及AT＆T人脸库的测试结果表明,在姿态、光照、表情、训练样本数目变化的情况下,ELPP都具有较好的识别率。     

监督式正交迹比判别投影在图像集人脸识别中的应用

研究、分析了人脸识别中提取原始数据特征的已有方法,在此基础上给出了一种应用监督式正交迹比判别投影(SOTRDP)的新型特征提取方法,即SOTRDP方法。不同于现有的非监督判别投影(UDP)方法,SOTRDP方法能够同时利用局部信息和类别信息建立相似性矩阵。在利用改进局部切空间对齐(ILTSA)非线性降维的基础上,利用聚类中心或最靠近它的样本作为输入,拓展SOTRDP用于图像集人脸识别。在PIE 和Honda/UCSD人脸数据库上的实验结果验证了所提方法的有效性。     

一个新的单参数填充函数算法

本文研究连续全局最优化问题的确定性求解方法.构造了一个单参数填充函数并证明了该填充函数的性质.该填充函数算法由极小化阶段和填充阶段两个阶段构成.其中极小化阶段利用局部优化方法获得填充函数的局部极小点,对填充函数的无约束极小化使得算法离开原目标函数的任何局部极小点.填充阶段依据原目标函数的局部极小点构造填充函数.极小化阶段和填充阶段交替重复实施直到终止准则满足.最后,给出了填充函数算法的数值结果.     

基于点模式匹配的前视目标定位算法

本文提出了一种基于点模式匹配技术的快速和鲁棒性的前视目标定位算法.该算法将前视目标定位问题转化为三维点集和二维点集的匹配问题.建立点集之间一一对应双向约束下的匹配目标函数,通过最小化该目标函数可以同时得到点集之间的匹配矩阵和变换参数.利用确定性退火算法中的退火温度来控制匹配矩阵的模糊度,增强了算法的鲁棒性,减小了陷入局部极小的可能性.实验结果验证了该算法的有效性和鲁棒性.     

基于全局-局部欧拉弹性判别投影的旋转机械故障诊断方法

故障诊断方法通常对异常值敏感，并且难以同时提取全局和局部判别信息，从而导致低维判别特征子集类间分离性不佳，针对该问题提出了一种基于全局-局部欧拉弹性判别投影(global-local euler elastic discriminant projection, GLEEDP)的旋转机械故障诊断方法。该方法通过余弦度量将高维故障特征映射到欧拉表示空间，扩大异类故障样本间的差异，然后在该空间中构建了基于全局、局部及类间散布三个目标函数的最优化模型，实现了在保持整体结构的基础上，进一步提高低维判别特征子集的局部类内聚集性和全局类间分离性。在轴承和齿轮箱两个机械故障数据集上的试验结果表明，所提方法可以有效发掘故障判别信息，具有优越的故障诊断性能。     

基于SILPDA的旋转机械故障诊断方法EI北大核心CSCD

针对故障特征集因“维数灾难”导致的故障分类困难现状,提出了一种基于强化内蕴局部保持判别分析(strengthened intrinsic local preserving discriminant analysis, SILPDA)的故障特征集降维算法。该算法将强化的多流形内蕴模型与局部相似度矩阵融入目标函数的构建中,期间充分考虑了数据集的多流形结构特征并且保留了样本的局部结构信息,使降维后的低维特征子集易于实施分类运算,继而实现提高故障辨识精度的效果。利用转子试验台振动信号集合构建的原始故障特征集对算法性能进行了验证。结果表明,该算法能够从原始故障数据集中提取出利于实施分类运算的敏感特征子集,这些特征子集将会使不同故障类别之间的边界变得更加清晰,最终相较于局部保持投影(locality preserving projections, LPP)、线性判别分析(linear discriminant analysis, LDA)、局部边缘判别投影(locality margin discriminant projection, LMDP)等算法可实现更好的故障辨识效果。对于提高旋转机械大数据资源的价值密度,该算法提供了一种优化数据结构模型的理论依据。     

Discriminant locality preserving projection chart for statistical monitoring of manufacturing processes

In this paper, we propose a novel multivariate projection chart called the discriminant locality preserving projection chart. The basic idea of the chart is to seek an optimal linear projection of the original data including both the in-control reference data and the newly observed data for monitoring. The projection strives to not only preserve the locality structure of the original data but also maximise the separation between the in-control reference data and the newly observed data. With this projection, the low-dimensional projected data will then be monitored through a T² type of statistics. Comparing with the existing projection-based control chart, the proposed chart preserves the local data structure and adaptively identifies the best projection direction to detect the out-of-control data, and thus has more discriminating power, particularly for non-linearly related multidimensional data. The design issues of this chart are discussed in details in this paper. The effectiveness of the proposed method is verified by numerical studies and a real case study of forging process monitoring.     

基于改进的线性判别分析识别重叠并发的双故障

在两维空间中,当关键质量特性之间存在相关关系并且预定义故障类之间重叠时,传统的模糊聚类算法FCM对双故障并发的识别率会下降。为了提升对重叠并发双故障的识别率,一种新算法PILDA被提出,该算法提出的主成分修整能够消除重叠的影响,而双故障判别区间确定的方法则能够实现对未预定义的并发双故障的识别。经过864种不同相关关系和均值偏移量的故障组合仿真实验,结果表明PILDA能有效识别并发故障及预定义单发故障,平均识别率为84.94%,明显高于FCM的58.13%。该方法具有一定的应用价值。     

Uncorrelated linear discriminant analysis (ULDA): A powerful tool for exploration of metabolomics data

The theory together with an algorithm for uncorrelated linear discriminant analysis (ULDA) is introduced and applied to explore metabolomics data. ULDA is a supervised method for feature extraction (FE), discriminant analysis (DA) and biomarker screening based on the Fisher criterion function. While principal component analysis (PCA) searches for directions of maximum variance in the data, ULDA seeks linearly combined variables called uncorrelated discriminant vectors (UDVs). The UDVs maximize the separation among different classes in terms of the Fisher criterion. The performance of ULDA is evaluated and compared with PCA, partial least squares discriminant analysis (PLS-DA) and target projection discriminant analysis (TP-DA) for two datasets, one simulated and one real from a metabolomic study. ULDA showed better discriminatory ability than PCA, PLS-DA and TP-DA. The shortcomings of PCA, PLS-DA and TP-DA are attributed to interference from linear correlations in data. PLS-DA and TP-DA performed successfully for the simulated data, but PLS-DA was slightly inferior to ULDA for the real data. ULDA successfully extracted optimal features for discriminant analysis and revealed potential biomarkers. Furthermore, by means of cross-validation, the classification model obtained by ULDA showed better predictive ability than PCA, PLS-DA and TP-DA. In conclusion, ULDA is a powerful tool for revealing discriminatory information in metabolomics data.     

一种新颖的基于Gabor-LDA的人脸识别方法

为了克服光照、表情变化等因素对人脸识别的影响,提出了一种基于Gabor小波和最佳鉴别分析LDA的人脸识别方法。该方法充分利用了LDA得到的鉴别向量,用鉴别向量组成线性变换矩阵,直接从原始的强度图像上提取LDA特征。然后,用鉴别向量选择一些鉴别像素,仅在鉴别像素的位置上提取Gabor特征并对Gabor特征作LDA变换得到另一种LDA特征。它们分别可视为全局特征和局部特征。最后的分类器融合这两类特征。在FERET人脸库上的试验表明了该方法的有效性。     

Matrix Discriminant Analysis With Application to Colorimetric Sensor Array Data

With the rapid development of nano-technology, a “colorimetric sensor array” (CSA) that is referred to as an optical electronic nose has been developed for the identification of toxicants. Unlike traditional sensors that rely on a single chemical interaction, CSA can measure multiple chemical interactions by using chemo-responsive dyes. The color changes of the chemo-responsive dyes are recorded before and after exposure to toxicants and serve as a template for classification. The color changes are digitalized in the form of a matrix with rows representing dye effects and columns representing the spectrum of colors. Thus, matrix-classification methods are highly desirable. In this article, we develop a novel classification method, matrix discriminant analysis (MDA), which is a generalization of linear discriminant analysis (LDA) for the data in matrix form. By incorporating the intrinsic matrix-structure of the data in discriminant analysis, the proposed method can improve CSA’s sensitivity and more importantly, specificity. A penalized MDA method, PMDA, is also introduced to further incorporate sparsity structure in discriminant function. Numerical studies suggest that the proposed MDA and PMDA methods outperform LDA and other competing discriminant methods for matrix predictors. The asymptotic consistency of MDA is also established. R code and data are available online as supplementary material.     

Efficient Integration of Sufficient Dimension Reduction and Prediction in Discriminant Analysis

Sufficient dimension reduction (SDR) methods are popular model-free tools for preprocessing and data visualization in regression problems where the number of variables is large. Unfortunately, reduce-and-classify approaches in discriminant analysis usually cannot guarantee improvement in classification accuracy, mainly due to the different nature of the two stages. On the other hand, envelope methods construct targeted dimension reduction subspaces that achieve dimension reduction and improve parameter estimation efficiency at the same time. However, little is known about how to construct envelopes in discriminant analysis models. In this article, we introduce the notion of the envelope discriminant subspace (ENDS) as a natural inferential and estimative object in discriminant analysis that incorporates these considerations. We develop the ENDS estimators that simultaneously achieve sufficient dimension reduction and classification. Consistency and asymptotic normality of the ENDS estimators are established, where we carefully examine the asymptotic efficiency gain under the classical linear and quadratic discriminant analysis models. Simulations and real data examples show superb performance of the proposed method. Supplementary materials for this article are available online.     

统计不相关最佳鉴别矢量集的本质研究

对统计不相关最佳鉴别矢量集的本质进行研究，在基于总体散布矩阵特征分解的基础上，构造了一种白化变换，使得变换后的样本空间中的总体散布矩阵为单位矩阵，这样使得传统的最佳鉴别矢量集算法得到的均是具有统计不相关的最佳鉴别矢量集，从而揭示了统计不相关最佳鉴别变换的本质——白化变换加普通的线性鉴别变换。该方法的最大优点在于所获得的最优鉴别矢量同时具有正交性和统计不相关性。该方法对代数特征抽取具有普遍适用性。用ORL人脸数据库的数值实验，验证了该方法的有效性。     

Enhanced Deep Autoencoder Based Feature Representation Learning for Intelligent Intrusion Detection System

In the era of Big data, learning discriminant feature representation from network traffic is identified has as an invariably essential task for improving the detection ability of an intrusion detection system (IDS). Owing to the lack of accurately labeled network traffic data, many unsupervised feature representation learning models have been proposed with state-of-the-art performance. Yet, these models fail to consider the classification error while learning the feature representation. Intuitively, the learnt feature representation may degrade the performance of the classification task. For the first time in the field of intrusion detection, this paper proposes an unsupervised IDS model leveraging the benefits of deep autoencoder (DAE) for learning the robust feature representation and one-class support vector machine (OCSVM) for finding the more compact decision hyperplane for intrusion detection. Specially, the proposed model defines a new unified objective function to minimize the reconstruction and classification error simultaneously. This unique contribution not only enables the model to support joint learning for feature representation and classifier training but also guides to learn the robust feature representation which can improve the discrimination ability of the classifier for intrusion detection. Three set of evaluation experiments are conducted to demonstrate the potential of the proposed model. First, the ablation evaluation on benchmark dataset, NSL-KDD validates the design decision of the proposed model. Next, the performance evaluation on recent intrusion dataset, UNSW-NB15 signifies the stable performance of the proposed model. Finally, the comparative evaluation verifies the efficacy of the proposed model against recently published state-of-the-art methods.     

一种改进NMF算法及其在人脸识别中的应用

为了提高非负矩阵分解(NMF)算法对光照、姿态等外部因素的鲁棒性,本文对传统的NMF进行改进,提出了一种改进的NMF方法.首先对NMF基图像进行判别分析,然后选择主要反应类内差异的基图像来构造子空间,最后在子空间上进行识别.通过Havard人脸库和Umist人脸库上的实验,结果表明,该方法能够对光照和姿态的变化具有一定的鲁棒性和较高的识别率,比传统的NMF方法和PCA等子空间分析法识别率提高了20%以上.