LPP solution schemes for use with face recognition

Locality preserving projection (LPP) is a manifold learning method widely used in pattern recognition and computer vision. The face recognition application of LPP is known to suffer from a number of problems including the small sample size (SSS) problem, the fact that it might produce statistically identical transform results for neighboring samples, and that its classification performance seems to be heavily influenced by its parameters. In this paper, we propose three novel solution schemes for LPP. Experimental results also show that the proposed LPP solution scheme is able to classify much more accurately than conventional LPP and to obtain a classification performance that is only little influenced by the definition of neighbor samples.     

On minimum class locality preserving variance support vector machine

In this paper, a so-called minimum class locality preserving variance support machine (MCLPV_SVM) algorithm is presented by introducing the basic idea of the locality preserving projections (LPP), which can be seen as a modified class of support machine (SVM) and/or minimum class variance support machine (MCVSVM). MCLPV_SVM, in contrast to SVM and MCVSVM, takes the intrinsic manifold structure of the data space into full consideration and inherits the characteristics of SVM and MCVSVM. We discuss in the paper the linear case, the small sample size case and the nonlinear case of the MCLPV_SVM. Similar to MCVSVM, the MCLPV_SVM optimization problem in the small sample size case is solved by using dimensionality reduction through principal component analysis (PCA) and one in the nonlinear case is transformed into an equivalent linear MCLPV_SVM problem under kernel PCA (KPCA). Experimental results on real datasets indicate the effectiveness of the MCLPV_SVM by comparing it with SVM and MCVSVM.     

保细节的网格刚性变形算法

提出了一种新的保细节的变形算法,可以使网格模型进行尽量刚性的变形,以减少变形中几何细节的扭曲.首先根据网格曲面局部细节的丰富程度,对原始网格进行聚类生成其简化网格;然后对简化网格进行变形,根据其相邻面片变形的相似性,对简化网格作进一步的合并,生成新的变形结果,将该变形传递给原始网格作为初始变形结果.由于对属于同一个类的网格顶点进行相同的刚性变形,可在变形中较好地保持该区域的表面细节,但分属不同类的顶点之间会出现变形的不连续.为此,通过迭代优化一个二次能量函数,对每个网格顶点的变形进行调整来得到最终变形结果.实验结果显示,该算法简单高效,结果令人满意.     

基于NPDM的保障系统顶层数据模型研究

介绍了北约产品数据模型(NPDM)的三层体系结构和EXPRESS数据建模语言,着重分析了NPDM顶层数据模型与产品寿命周期的关系,模型中各模式记录的信息内容和模式之间的关系.依据NPDM思想建立了保障系统顶层数据模型,阐述了模型中各模式记录的信息内容,给出了人力与人员、保障设备和保障设施等保障要素的数据结构,为进一步建立保障系统数据模型的打下了基础.     

基于多项式根最大模求解的二分搜索算法

传统多项式根最大模求解算法的求解效率低、计算复杂.针对该问题,提出一种基于多项式根的最大模求解的二分搜索算法.该算法通过选取模的上下界确定初始搜索区间,利用判定定理判断多项式的根与单位圆的关系,从而求得多项式任意精度的最大模.仿真结果表明,该算法收敛速度快、求解精度高.     

基于法向量的非线性逼近型细分格式

提出一种二进制的几何非线性逼近型细分格式。在该格式中,新点不全是旧点的线性组合,其中一个新点是通过在法向量方向偏移所产生,且法向量在每次细分中能自适应计算。引入一些参数来控制细分过程,且参数对曲线形状的影响是局部的。实例证明,通过选择适当的参数值,产生的细分曲线具有保凸性和 连续性。     

基于成对约束和稀疏保留的数据降维算法

结合以成对约束形式给出的监督信息和无监督信息,提出一种基于成对约束和稀疏保留的数据降维算法。通过成对约束信息进行鉴别分析,利用稀疏表示方法保留数据集在变换空间中的全局稀疏结构。实验结果表明,与传统特征抽取算法相比,该算法的识别效果更好,需要调节的参数更少,且鲁棒性较高。     

Saturation in multivariate simultaneous approximation

In this paper we present a new result on the saturation of sequences of linear operators in a multivariate and simultaneous setting. Specifically, a small o saturation result is obtained for the partial derivatives of the classical Bernstein bivariate operators on the unit simplex. Solutions of boundary value problems for certain partial differential equations of elliptic type play an important role.     

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.     

网上交易中的电文固化系统设计

为应对网上交易等电子商务等存在安全问题,确保交易安全,并面对可靠的固化和保留交易所产生的电文作为交易证据进行保全和固化的需求,设计了一种电文固化系统,包含其网络拓扑架构,及所依赖的安全网络传输协议.整个系统采用电文固化中心和在交易平台方所设的电文固化本地代理的分布式结构.交易平台方在遇到电文固化需求时,通过本地代理与电...