Introduction of a nonlinearity measure for principal component models

Although principal component analysis (PCA) is an important tool in standard multivariate data analysis, little interest has been devoted to assessing whether the underlying relationship within a given variable set can be described by a linear PCA model or whether nonlinear PCA must be utilized. This paper addresses this deficiency by introducing a nonlinearity measure for principal component models. The measure is based on the following two principles: (i) the range of recorded process operation is divided into smaller regions; and (ii) accuracy bounds are determined for the sum of the discarded eigenvalues. If this sum is within the accuracy bounds for each region, the process is assumed to be linear and vice versa. This procedure is automated through the use of cross-validation. Finally, the paper shows the utility of the new nonlinearity measure using two simulation studies and with data from an industrial melter process.     

Prediction of Final Product Properties After Cocurrent Spray Drying

The effect of drying and atomization conditions on the physical properties of powders for agglomerate-like materials and skin-forming material are studied in this article. A neural model is used for powder bulk and tapped density predictions.     

A new curvature technique calculation for surface tension contribution in PLIC-VOF method

The volume of fluid (VOF) methods have been used for numerous numerical simulations. Among these techniques used to define the moving interface, the piecewise linear interface reconstruction (PLIC-VOF) is one of the most accurate. A study of the superficial tension impact on two-phase flow with free surface is presented. A new method based on direct staggered grid is developped to include surface tension in PLIC-VOF. The new numerical curvature calculation method doesn't need smoothed colour function and leads to less “spurious current”. This technique is applied to the calculus of surface tension force in the case of the rise of air bubble in viscous liquid and the fall of liquid drop in the same liquid on free surface. Droplets, thin layer and capillarity waves are observed after the free surface rupture for different Bond number. The influence of surface tension calculus is then obvioused and when the drop hit the free surface, wavelets propagate toward the virtual boundaries imposed.     

On principal component analysis, cosine and Euclidean measures in information retrieval

Clustering groups document objects represented as vectors. An extensive vector space may cause obstacles to applying these methods. Therefore, the vector space was reduced with principal component analysis (PCA). The conventional cosine measure is not the only choice with PCA, which involves the mean-correction of data. Since mean-correction changes the location of the origin, the angles between the document vectors also change. To avoid this, we used a connection between the cosine measure and the Euclidean distance in association with PCA, and grounded searching on the latter. We applied the single and complete linkage and Ward clustering to Finnish documents utilizing their relevance assessment as a new feature. After the normalization of the data PCA was run and relevant documents were clustered.     

3D model retrieval using principal plane analysis and dynamic programming

Three dimensional models play an important role in many applications; the problem is how to select the appropriate models from a 3D database rapidly and accurately. In recent years, a variety of shape representations, statistical methods, and geometric algorithms have been proposed for matching 3D shapes or models. In this paper, we propose a 3D shape representation scheme based on a combination of principal plane analysis and dynamic programming. The proposed 3D shape representation scheme consists of three steps. First, a 3D model is transformed into a 2D image by projecting the vertices of the model onto its principal plane. Second, the convex hall of the 2D shape of the model is further segmented into multiple disjoint triangles using dynamic programming. Finally, for each triangle, a projection score histogram and moments are extracted as the feature vectors for similarity searching. Experimental results showed the robustness of the proposed scheme, which resists translation, rotation, scaling, noise, and destructive attacks. The proposed 3D model retrieval method performs fairly well in retrieving models having similar characteristics from a database of 3D models.     

基于遥感案例推理的海岸带养殖信息提取

目前基于目视解释或光谱分类的养殖信息提取效率低,难以克服由于地物混杂带来的“椒盐”噪声现象且难以融合地学知识。针对养殖信息提取中存在的问题,首先在分析现有养殖信息提取方法和案例推理CBR(Case\|Based Reasoning)用于遥感图像处理的基础上,提出基于遥感案例推理的海岸带养殖信息提取的研究思路;其次,结合养殖区域的空间特征和属性特征,构建案例的表达模型以及CBR相似性推理模型;最后,对不属于案例构建区的粤西沙田镇进行养殖信息提取的CBR实验,精度达到84.56%。对比CBR方法和传统监督分类方法可知,CBR方法是实现海岸带养殖信息快速准确提取的一种有效手段。     

Weighted principal component extraction with genetic algorithms

Pattern recognition techniques have been widely used in a variety of scientific disciplines including computer vision, artificial intelligence, biology, and so forth. Although many methods present satisfactory performances, they still have several weak points, thus leaving a lot of space for further improvements. In this paper, we propose two performance-driven subspace learning methods by extending the principal component analysis (PCA) and the kernel PCA (KPCA). Both methods adopt a common structure where genetic algorithms are employed to pursue optimal subspaces. Because the proposed feature extractors aim at achieving high classification accuracy, enhanced generalization ability can be expected. Extensive experiments are designed to evaluate the effectiveness of the proposed algorithms in real-world problems including object recognition and a number of machine learning tasks. Comparative studies with other state-of-the-art techniques show that the methods in this paper are capable of enhancing generalization ability for pattern recognition systems.     

An ExPosition of multivariate analysis with the singular value decomposition in R

ExPosition is a new comprehensive R package providing crisp graphics and implementing multivariate analysis methods based on the singular value decomposition (svd). The core techniques implemented in ExPosition are: principal components analysis, (metric) multidimensional scaling, correspondence analysis, and several of their recent extensions such as barycentric discriminant analyses (e.g., discriminant correspondence analysis), multi-table analyses (e.g.,multiple factor analysis, Statis, and distatis), and non-parametric resampling techniques (e.g., permutation and bootstrap). Several examples highlight the major differences between ExPosition and similar packages. Finally, the future directions of ExPosition are discussed.