保局投影在图像隐密检测中的应用

为解决图像隐密检测中图像特征维数过高导致的"维数灾难"问题,在保持图像特征内在低维结构的基础上降低特征向量的维数,方便构造更有效的分类器,提出了一种基于保局投影(locality preserving projections,LPP)降维的图像隐密检测算法,对待测图像进行小波变换形成图像特征后,利用LPP算法实现对图像高维特征的降维,得到图像特征集的低维流形.使用支持向量机(SVM)算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类.实验结果表明,与不使用降维算法的检测方案相比,基于LPP降维的检测算法能够显著地提高检测的准确率.     

超光谱遥感图像有监督LPP特征提取研究

在局部保留投影(LPP)特征提取算法的基础上,利用样本标签信息提出了一种有监督的局部保留投影算法(SPLPP),该算法的邻接图的权值不仅考虑了LPP算法中的相似性权值,而且加入了监督类的相关权值。SPLPP算法主要步骤是先用PCA去除高维超光谱遥感图像的冗余信息,再把监督机制引入到LPP中,实现图像的特征提取,将高维超光谱遥感图像投影到低维空间中,利于分类。应用SPLPP算法对高维的遥感原始超光谱图像进行特征提取后,利用支持向量机(SVM)和最近邻分类器(KNN)对降维后的遥感图像数据进行分类;并与PCA、LPP、LDA等特征提取算法进行了比较实验。实验表明:结合了LPP局部信息保留能力和全域标签信息的SPLPP算法,有更好的局部信息保留能力和类判别能力,使分类器分类精度更高,分类效果更好。     

应用于人脸识别的改进局部保持投影算法

局部保持投影算法（locality preserving projections,LPP）作为降维算法,在机器学习和模式识别中有着广泛应用。在识别分类中,为了更好的利用类别信息,在保持样本点的局部特征外,有效地从高维数据中提取出低维的人脸图像信息并提高人脸图像的识别率和识别速度,使分类达到一定优化,基于LPP算法结合流形学习思想,通过构造一种吸引向量的方法提出一种改进的局部保持投影算法（reformation locality preserve projections ,RLPP）。将数据集利用极端学习机分类器进行分类后,在标准人脸数据库上的进行试验,实验结果证明,改进后算法的识别率优于LPP算法、局部保持平均邻域边际最大化算法和鲁棒线性降维算法,具有较强的泛化能力和较高的识别率。     

基于监督局部线性嵌入特征提取的高光谱图像分类

高光谱图像的数据维数高、数据量大、数据间高度冗余等特点给图像分类带来困难,为进行有效降维、提高分类精度,提出了一种监督局部线性嵌入(SLLE)非线性流形学习特征提取方法。SLLE算法根据数据先验类标签信息所给出的新距离寻找数据点的k最近邻(NN),新距离使得类内距离小于类间距离,这使得SLLE算法更有利于分类。高光谱图像数据和UCI数据的分类结果表明了该方法的有效性。     

基于局部保持映射的图像隐密检测算法

提出了一种新的基于局部保持映射（Locality Preserving Projections,LPP）降维的图像隐密检测方案。为降低图像特征向量的维数,同时保持其内在低维结构,方便构造更有效的分类器,在经过小波变换形成图像特征后,利用LPP算法得到图像特征集的低维流形,实现对图像高维特征的降维。进而使用支持向量机（SVM）算法将降维后的特征映射到分类特征空间,实现对正常图像和隐密图像分类。实验结果表明,与不采用降维算法的检测方案相比,提出的方案能够显著地提高检测的准确率。     

基于LFDA和GA-ELM的高光谱图像地物识别方法研究

高光谱图像的高维特性和波段间的高相关性,导致高光谱图像地物识别问题研究中,面临着数据量大、信息冗余的问题,降低了高光谱图像的分类识别精度。针对以上问题,提出了基于局部保留降维（Local Fisher Discriminant Analysis,LFDA）结合遗传算法（Genetic Algorithm, GA ）优化极限学习机（Extreme Learning Machine, ELM）的高光谱图像分类方法。首先,采用LFDA对高光谱图像数据进行降维处理,消除信息冗余并保留局部邻域内主要特征;然后用GA优化ELM,对降维处理后的特征样本进行分类,提高高光谱图像的分类识别精度。将该方法应用于Salinas和Pavia University高光谱图像的地物识别问题研究,分类精度分别达到了98.56%和97.11%,由此验证了该方法的有效性。     

基于多视图边界判别投影的高光谱图像分类

高光谱图像分类是遥感领域研究的热点问题,其关键在于利用高光谱图谱合一的 优势,同时融合高光谱图像中各个像元位置的光谱信息和空间信息,提高光谱图像分类精度。 针对高光谱图像特征维数高和冗余信息多等问题,采用多视图子空间学习方法进行特征降维, 提出了图正则化的多视图边界判别投影算法。将每个像元处的光谱特征看作一个视图,该像元 处的空间特征看作另一个视图,通过同时优化每个视图上的投影方向来寻找最优判别公共子空 间。公开测试数据集上的分类实验表明,多视图学习在高光谱图像空谱融合分类方面具有显著 的优越性,在多视图降维算法中,该算法具有最高的分类准确性。     

基于同质性降维和CMP算法的高光谱图像分类

针对高光谱数据维数高,波段间冗余信息大的问题,提出一种基于同质性降维和组合匹配追踪算法的高光谱图像分类方法。该方法首先利用均值漂移算法对高光谱图像进行分割得到同质性图像块,对同质性的图像块进行流行学习得到降维映射函数,然后由降维后的高光谱数据训练稀疏最小二乘支持向量机分类模型,为避免正交匹配追踪稀疏重构算法迭代次数多的缺点,提出一种基于组合匹配追踪的稀疏重构求解方法。通过高光谱数据的分类结果可以得出,该方法有效提高了高光谱图像的分类精度。     

基于LPP的工业过程故障检测

针对传统的降维算法在降维过程中存在着丢失数据的局部邻域信息的问题,一种基于局部保持投影(LPP)用于工业工程数据检测的方法被应用。LPP算法的思想是通过构造数据样本点之间的亲疏关系,并且在投影降维的同时保留数据样本点的这种局部邻域结构,从而保留数据的局部信息。论文将LPP算法与传统的降维算法-主元分析法(P CA)在田纳西-伊斯曼过程(T EP)仿真系统上进行检测对比,结果表明,LPP算法具有更加优越的检测性能。     

基于局部保持投影的复合位置投影

为解决在人脸识别领域的特征提取问题,提出一种基于局部保持投影（LPP）的复合位置投影（MLPP）方法,通过选取不同的类内、类间度量矩阵和约束矩阵,将求解最优变换矩阵的问题转换成普通的特征值问题。在构造邻接图时,该算法将相同类各点作为邻接点,将类内结构保持到特征空间中,在保留局部结构稳定的同时,使整体结构趋于最大化,从而形成高效的聚簇。在AT&T和JAFFE标准人脸图像库上的实验结果表明,MLPP算法具有较高的识别率。     

Multivariate statistical process control based on multiway locality preserving projections

An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T² (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data.     

Graph-optimized locality preserving projections

Locality preserving projections (LPP) is a typical graph-based dimensionality reduction (DR) method, and has been successfully applied in many practical problems such as face recognition. However, LPP depends mainly on its underlying neighborhood graph whose construction suffers from the following issues: (1) such neighborhood graph is artificially defined in advance, and thus does not necessary benefit subsequent DR task; (2) such graph is constructed using the nearest neighbor criterion which tends to work poorly due to the high-dimensionality of original space; (3) it is generally uneasy to assign appropriate values for the neighborhood size and heat kernel parameter involved in graph construction. To address these problems, we develop a novel DR algorithm called Graph-optimized Locality Preserving Projections (GoLPP). The idea is to integrate graph construction with specific DR process into a unified framework, which results in an optimized graph rather than predefined one. Moreover, an entropy regularization term is incorporated into the objective function for controlling the uniformity level of the edge weights in graph, so that a principled graph updating formula naturally corresponding to conventional heat kernel weights can be obtained. Finally, the experiments on several publicly available UCI and face data sets show the feasibility and effectiveness of the proposed method with encouraging results.     

Sparsity preserving projections with applications to face recognition

Dimensionality reduction methods (DRs) have commonly been used as a principled way to understand the high-dimensional data such as face images. In this paper, we propose a new unsupervised DR method called sparsity preserving projections (SPP). Unlike many existing techniques such as local preserving projection (LPP) and neighborhood preserving embedding (NPE), where local neighborhood information is preserved during the DR procedure, SPP aims to preserve the sparse reconstructive relationship of the data, which is achieved by minimizing a L1 regularization-related objective function. The obtained projections are invariant to rotations, rescalings and translations of the data, and more importantly, they contain natural discriminating information even if no class labels are provided. Moreover, SPP chooses its neighborhood automatically and hence can be more conveniently used in practice compared to LPP and NPE. The feasibility and effectiveness of the proposed method is verified on three popular face databases (Yale, AR and Extended Yale B) with promising results.     

基于局部相似性的视频镜头分割*

为了进一步提高视频镜头的分割精度,提出了一种基于局部相似性的视频镜头分割方法。首先为了有效地进行视频特征降维,提出了改进的保局投影算法,利用仿射传播聚类算法得到具有相同模式的相似样本,根据相似样本构建连接矩阵,并根据降维前后模式的相关系数确定最佳降维维数,该算法有效地保留了数据的局部分布信息;然后利用具有相同模式的相似样本构建局部支持向量机检测镜头边界。实验结果表明,该方法利用样本的局部相似性特点,在视频特征提取和镜头边界检测两个阶段提高了镜头分割精度。     

Enhanced locality preserving projections using robust path based similarity

Curse of dimensionality is a bothering problem in high dimensional data analysis. To enhance the performances of classification or clustering on these data, their dimensionalities should be reduced beforehand. Locality Preserving Projections (LPP) is a widely used linear dimensionality reduction method. It seeks a subspace in which the neighborhood graph structure of samples is preserved. However, like most dimensionality reduction methods based on graph embedding, LPP is sensitive to noise and outliers, and its effectiveness depends on choosing suitable parameters for constructing the neighborhood graph. Unfortunately, it is difficult to choose effective parameters for LPP. To address these problems, we propose an Enhanced LPP (ELPP) using a similarity metric based on robust path and a Semi-supervised ELPP (SELPP) with pairwise constraints. In comparison with original LPP, our methods are not only robust to noise and outliers, but also less sensitive to parameters selection. Besides, SELPP makes use of pairwise constraints more efficiently than other comparing methods. Experimental results on real world face databases confirm their effectiveness.     

A Minimal Line Property Preserving Representation of Line Images

In line image understanding a minimal line property preserving (MLPP) graph of the image compliments the structural information in geometric graph representations like the run graph. With such a graph and its dual it is possible to efficiently detect topological features like loops and holes and to make use of relations like containment. We present a new rule based method on dual graph contraction for transforming the run graph and its dual into MLPP graphs. A parallel O(log(longest curve)) algorithm is presented and results given. Received: May 28, 1998; revised November 17, 1998     

基于判别正则化的局部保留投影

局部保留投影（Locality preserving projections,LPP）是一种常用的线性化流形学习方法,其通过线性嵌入来保留基于图所描述的流形数据本质结构特征,因此LPP对图的依赖性强,且在嵌入过程中缺少对图描述的进一步分析和挖掘。当图对数据本质结构特征描述不恰当时,LPP在嵌入过程中不易实现流形数据本质结构的有效提取。为了解决这个问题,本文在给定流形数据图描述的条件下,通过引入局部相似度阈值进行局部判别分析,并据此建立判别正则化局部保留投影（简称DRLPP）。该方法能够在现有图描述的条件下,有效突出不同流形结构在线性嵌入空间中的可分性。在人造合成数据集和实际标准数据集上对DRLPP以及相关算法进行对比实验,实验结果证明了DRLPP的有效性。     

Semi-supervised dimension reduction based on hypergraph embedding for hyperspectral images

Dimension reduction (DR) is an efficient and effective preprocessing step of hyperspectral images (HSIs) classification. Graph embedding is a frequently used model for DR, which preserves some geometric or statistical properties of original data set. The embedding using simple graph only considers the relationship between two data points, while in real-world application, the complex relationship between several data points is more important. To overcome this problem, we present a linear semi-supervised DR method based on hypergraph embedding (SHGE) which is an improvement of semi-supervised graph learning (SEGL). The proposed SHGE method aims to find a projection matrix through building a semi-supervised hypergraph which can preserve the complex relationship of the data and the class discrimination for DR. Experimental results demonstrate that our method achieves better performance than some existing DR methods for HSIs classification and is time saving compared with the existed method SEGL which used simple graph.     

一种基于局部保持的隐变量模型

隐变量模型是一类有效的降维方法,但是由非线性核映射建立的隐变量模型不能保持数据空间的局部结构。为了克服这个缺点,文中提出一种保持数据局部结构的隐变量模型。该算法充分利用局部保持映射的保局性质,将局部保持映射的目标函数作为低维空间中数据的先验信息,对高斯过程隐变量中的低维数据进行约束,建立局部保持的隐变量。实验结果表明,相比原有的高斯过程隐变量,文中算法较好地保持数据局部结构的效果。