On data depth in infinite dimensional spaces

The concept of data depth leads to a center-outward ordering of multivariate data, and it has been effectively used for developing various data analytic tools. While different notions of depth were originally developed for finite dimensional data, there have been some recent attempts to develop depth functions for data in infinite dimensional spaces. In this paper, we consider some notions of depth in infinite dimensional spaces and study their properties under various stochastic models. Our analysis shows that some of the depth functions available in the literature have degenerate behaviour for some commonly used probability distributions in infinite dimensional spaces of sequences and functions. As a consequence, they are not very useful for the analysis of data satisfying such infinite dimensional probability models. However, some modified versions of those depth functions as well as an infinite dimensional extension of the spatial depth do not suffer from such degeneracy and can be conveniently used for analyzing infinite dimensional data.     

基于可拓学理论的高维大数据相似性研究

高维大数据的相似性计算是数据挖掘领域的研究重点,论文通过分析高维大数据相似性计算的难点,提出采用可拓学的方法解决其中矛盾问题的研究思路。在基元表示高维大数据的基础上,借助数据转换、数据筛选、权重的确定、数据预处理等技术实现了数据之间的相似性计算,并基于水污染常规分析数据进行了算法验证。论文借助可拓的思想研究大数据相似性的问题,不仅对数据挖掘的研究有一定的理论促进,同时也为可拓学的研究提供了新的应用空间。     

Integration and dimensional modeling approaches for complex data warehousing

With the broad development of the World Wide Web, various kinds of heterogeneous data (including multimedia data) are now available to decision support tasks. A data warehousing approach is often adopted to prepare data for relevant analysis. Data integration and dimensional modeling indeed allow the creation of appropriate analysis contexts. However, the existing data warehousing tools are well-suited to classical, numerical data. They cannot handle complex data. In our approach, we adapt the three main phases of the data warehousing process to complex data. In this paper, we particularly focus on two main steps in complex data warehousing. The first step is data integration. We define a generic UML model that helps representing a wide range of complex data, including their possible semantic properties. Complex data are then stored in XML documents generated by a piece of software we designed. The second important phase we address is the preparation of data for dimensional modeling. We propose an approach that exploits data mining techniques to assist users in building relevant dimensional models.     

Development of three dimensional constitutive theories based on lower dimensional experimental data

Most three dimensional constitutive relations that have been developed to describe the behavior of bodies are correlated against one dimensional and two dimensional experiments. What is usually lost sight of is the fact that infinity of such three dimensional models may be able to explain these experiments that are lower dimensional. Recently, the notion of maximization of the rate of entropy production has been used to obtain constitutive relations based on the choice of the stored energy and rate of entropy production, etc. In this paper we show different choices for the manner in which the body stores energy and dissipates energy and satisfies the requirement of maximization of the rate of entropy production that can all describe the same experimental data. All of these three dimensional models, in one dimension, reduce to the model proposed by Burgers to describe the viscoelastic behavior of bodies.      

Hybrid high dimensional model representation (HHDMR) on the partitioned data

A multivariate interpolation problem is generally constructed for appropriate determination of a multivariate function whose values are given at a finite number of nodes of a multivariate grid. One way to construct the solution of this problem is to partition the given multivariate data into low-variate data. High dimensional model representation (HDMR) and generalized high dimensional model representation (GHDMR) methods are used to make this partitioning. Using the components of the HDMR or the GHDMR expansions the multivariate data can be partitioned. When a cartesian product set in the space of the independent variables is given, the HDMR expansion is used. On the other hand, if the nodes are the elements of a random discrete data the GHDMR expansion is used instead of HDMR. These two expansions work well for the multivariate data that have the additive nature. If the data have multiplicative nature then factorized high dimensional model representation (FHDMR) is used. But in most cases the nature of the given multivariate data and the sought multivariate function have neither additive nor multiplicative nature. They have a hybrid nature. So, a new method is developed to obtain better results and it is called hybrid high dimensional model representation (HHDMR). This new expansion includes both the HDMR (or GHDMR) and the FHDMR expansions through a hybridity parameter. In this work, the general structure of this hybrid expansion is given. It has tried to obtain the best value for the hybridity parameter. According to this value the analytical structure of the sought multivariate function can be determined via HHDMR.     

Sample covariance shrinkage for high dimensional dependent data

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce variance. In this paper, we give details on feasible optimal shrinkage allowing for time series dependent observations.     

Three‐dimensional blockmodels

In the classic blockmodel formulation, a social network among members of a population with n actors and k relations (types of tie) is arrayed as k n X n matrices. Though this is a three‐dimensional data structure, it is typically reduced to a two‐way analysis. In this paper, a three‐way procedure for analyzing multigraph data is developed. Specifically, in addition to applying the rule of structural equivalence to collapse actors, it is also applied to the relations (the third dimension), and structurally equivalent sets of relations are collapsed. The result is a three‐dimensional blockmodel (image) of social structure that is a more parsimonious representation of social structure than the classic two‐dimensional blockmodel images. The three‐dimensional approach is illustrated by application to three case studies: Homan's Bank Wiring Room, Sampson's monastery, and a local economy of hospital services. The structural equivalence approach to relations is further explored by applying it to the individual‐level Liking and Antagonism relations and their compounds (of length four or less) in the Bank Wiring Room. This application demonstrates that the structural equivalence approach can be used to identify equality equations for primitive and compound relations.     

The Singular Structure of Non-selfsimilar Global Solutions of n Dimensional Burgers Equation

We investigate the singular structure for n dimensional non-selfsimilar global solutions and interaction of non-selfsimilar elementary wave of n dimensional Burgers equation, where the initial discontinuity is a n dimensional smooth surface and initial data just contain two different constant states, global solutions and some new phenomena are discovered. An elegant technique is proposed to construct n dimensional shock wave without dimensional reduction or coordinate transformation.     

Principal differential analysis of the Aneurisk65 data set

We explore the use of principal differential analysis as a tool for performing dimensional reduction of functional data sets. In particular, we compare the results provided by principal differential analysis and by functional principal component analysis in the dimensional reduction of three synthetic data sets, and of a real data set concerning 65 three-dimensional cerebral geometries, the AneuRisk65 data set. The analyses show that principal differential analysis can provide an alternative and effective representation of functional data, easily interpretable in terms of exponential, sinusoidal, or damped-sinusoidal functions and providing a different insight to the functional data set under investigation. Moreover, in the analysis of the AneuRisk65 data set, principal differential analysis is able to detect interesting features of the data, such as the rippling effect of the vessel surface, that functional principal component analysis is not able to detect.     

An efficient random forests algorithm for high dimensional data classification

In this paper, we propose a new random forest (RF) algorithm to deal with high dimensional data for classification using subspace feature sampling method and feature value searching. The new subspace sampling method maintains the diversity and randomness of the forest and enables one to generate trees with a lower prediction error. A greedy technique is used to handle cardinal categorical features for efficient node splitting when building decision trees in the forest. This allows trees to handle very high cardinality meanwhile reducing computational time in building the RF model. Extensive experiments on high dimensional real data sets including standard machine learning data sets and image data sets have been conducted. The results demonstrated that the proposed approach for learning RFs significantly reduced prediction errors and outperformed most existing RFs when dealing with high-dimensional data.     

RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections

In high dimensions, the classical Hotelling’s T² test tends to have low power or becomes undefined due to singularity of the sample covariance matrix. In this article, this problem is overcome by projecting the data matrix onto lower dimensional subspaces through multiplication by random matrices. We propose RAPTT (RAndom Projection T²-Test), an exact test for equality of means of two normal populations based on projected lower dimensional data. RAPTT does not require any constraints on the dimension of the data or the sample size. A simulation study indicates that in high dimensions the power of this test is often greater than that of competing tests. The advantages of RAPTT are illustrated on a high-dimensional gene expression dataset involving the discrimination of tumor and normal colon tissues.     

m维AR(p)模型的统计诊断

统计诊断就是探查对统计推断（如估计或预测等）有较大影响的数据从而对全过程数据进行诊断．本文应用基于数据删除模型得到二维AR（1）模型的参数估计诊断公式,给出了Cook统计量的计算公式,进而推广到m维AR（p）模型的情形．     

Analytic centers and repelling inequalities

The new concepts of repelling inequalities, repelling paths, and prime analytic centers are introduced. A repelling path is a generalization of the analytic central path for linear programming, and we show that this path has a unique limit. Furthermore, this limit is the prime analytic center if the set of repelling inequalities contains only those constraints that “shape” the polytope. Because we allow lower dimensional polytopes, the proof techniques are non-standard and follow from data perturbation analysis. This analysis overcomes the difficulty that analytic centers of lower dimensional polytopes are not necessarily continuous with respect to the polytope's data representation. A second concept introduced here is that of the “prime analytic center”, in which we establish its uniqueness in the absence of redundant inequalities. Again, this is well known for full dimensional polytopes, but it is not immediate for lower dimensional polytopes because there are many different data representations of the same polytope, each without any redundant inequalities. These two concepts combine when we introduce ways in which repelling inequalities can interact.     

Dimensional reduction for Helmholtz's equation on a bounded domain

Summary. The dimensional reduction method for solving boundary value problems of Helmholtz's equation in domain by replacing them with systems of equations in dimensional space are investigated. It is proved that the existence and uniqueness for the exact solution and the dimensionally reduced solution of the boundary value problem if the input data on the faces are in some class of functions. In addition, the difference between and in is estimated as and are fixed. Finally, some numerical experiments in a domain are given in order to compare theretical results. Received April 2, 1996 / Revised version received July 30, 1990     

A uniform estimate for the MMOC for two‐dimensional advection‐diffusion equations

We prove an optimal‐order error estimate in a weighted energy norm for the modified method of characteristics (MMOC) and the modified method of characteristics with adjusted advection (MMOCAA) for two‐dimensional time‐dependent advection‐diffusion equations, in the sense that the generic constants in the estimates depend on certain Sobolev norms of the true solution but not on the scaling diffusion parameter ε. © 2009 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 2010     

Dynamic stability of the three‐dimensional axisymmetric Navier‐Stokes equations with swirl

In this paper, we study the dynamic stability of the three‐dimensional axisymmetric Navier‐Stokes Equations with swirl. To this purpose, we propose a new one‐dimensional model that approximates the Navier‐Stokes equations along the symmetry axis. An important property of this one‐dimensional model is that one can construct from its solutions a family of exact solutions of the three‐dimensionaFinal Navier‐Stokes equations. The nonlinear structure of the one‐dimensional model has some very interesting properties. On one hand, it can lead to tremendous dynamic growth of the solution within a short time. On the other hand, it has a surprising dynamic depletion mechanism that prevents the solution from blowing up in finite time. By exploiting this special nonlinear structure, we prove the global regularity of the three‐dimensional Navier‐Stokes equations for a family of initial data, whose solutions can lead to large dynamic growth, but yet have global smooth solutions. © 2007 Wiley Periodicals, Inc.     

A finite element approach for modelling hip joint contact

In this contribution a finite element model for the three dimensional investigation of hip joint contact is described. A shell-like interface element with variable thickness is developed for modelling fluid flow in the synovial gap. For this purpose the Taylor-Hood element is extended in order to take a spatial thickness distribution and local thickness changes into account. The interaction between the synovial fluid and the cartilage layers is solved by a staggered iteration using an artificial compressibility method. Cartilage is modelled using the theory of porous media and three dimensional geometries are reconstructed from medical imaging data. (© 2011 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

SWAMP: A two‐dimensional hydrodynamic and quality modeling platform for shallow waters

Water quality two‐dimensional models are often partitioned into separate modules with separate hydraulic and biological units. In most cases this approach results in poor flexibility whenever the biological dynamics has to be adapted to a specific situation. Conversely, an integrated approach is pursued in this article, producing a two‐dimensional hydraulic‐water quality model, named Shallow Water Analysis and Modeling Program (SWAMP) designed for shallow water bodies. The major objective of the work is to create a comprehensive two‐dimensional water quality assessment tool, based on an open framework and combining easy programming of additional procedures with a user‐friendly interface. The model is based on the numerical solution of the partial differential equations describing advection‐diffusion and biological processes on a two‐dimensional rectangular finite elements mesh. The hydraulics and advection‐diffusion modules model were validated both with experimental tracer data collected at a constructed wetland site and a comparison with a commercial hydrodynamic software, showing good agreement in both cases. Moreover, the model was tested in critical conditions for mass conservation, such as time‐varying wet boundary, showing a considerable numerical robustness. In the last part of the article water quality simulations are presented, though validation data are not yet available. Nevertheless, the observed model response demonstrates general consistency with expected results and the advantages of integrating the hydraulic and quality modules. The interactive graphical user interface (GUI) is also shown to represent a simple and effective connective tool to the integrated package. © 2002 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 18: 663–687, 2002; DOI 10.1002/num.10014     

Lower Dimensional Representation of Text Data Based on Centroids and Least Squares

Dimension reduction in today's vector space based information retrieval system is essential for improving computational efficiency in handling massive amounts of data. A mathematical framework for lower dimensional representation of text data in vector space based information retrieval is proposed using minimization and a matrix rank reduction formula. We illustrate how the commonly used Latent Semantic Indexing based on the Singular Value Decomposition (LSI/SVD) can be derived as a method for dimension reduction from our mathematical framework. Then two new methods for dimension reduction based on the centroids of data clusters are proposed and shown to be more efficient and effective than LSI/SVD when we have a priori information on the cluster structure of the data. Several advantages of the new methods in terms of computational efficiency and data representation in the reduced space, as well as their mathematical properties are discussed.Experimental results are presented to illustrate the effectiveness of our methods on certain classification problems in a reduced dimensional space. The results indicate that for a successful lower dimensional representation of the data, it is important to incorporate a priori knowledge in the dimension reduction algorithms.     

Global existence for some transport equations with nonlocal velocity

In this paper, we study transport equations with nonlocal velocity fields with rough initial data. We address the global existence of weak solutions of a one dimensional model of the surface quasi-geostrophic equation and the incompressible porous media equation, and one dimensional and n dimensional models of the dissipative quasi-geostrophic equations and the dissipative incompressible porous media equation.