On the Time of Completion of Pursuit in One Nonlinear Differential Game

For a nonlinear antagonistic two-person differential game on a manifold, we propose a method for the solution of the pursuit problem and determine the time of guaranteed capture.     

教学园地积分学的一个美学追求

本文建立翻转变换概念,借此实现积分学的一个美学追求--改善定积分性质中关于被积函数与积分区间的条款不对称的局面,进而给出一个计算定积分的典型方法.     

投影寻踪回归在试验设计分析中的应用研究

本文对三个应用实例进行分析，研究结果表明：ＰＰＲ方法是试验设计回归分析行之有效的方法，它给技术工作者提供了一个处理试验数据的崭新统计方法。ＰＰＲ的应用，将会大幅度降低试验费用，提高试验研究工作效率。     

积分学的一个美学追求

本文建立翻转变换概念 ,借此实现积分学的一个美学追求——改善定积分性质中关于被积函数与积分区间的条款不对称的局面 ,进而给出一个计算定积分的典型方法 .     

投影寻踪方法与高光谱遥感图像数据特征提取的研究

高光谱技术的发展与应用对高维的高光谱遥感数据的处理方法提出了更高的要求，投影寻踪方法是一种处理高维数据的十分有效的降维方法。高光谱图像分析对投影寻踪方法仍是一个全新的领域。本文介绍了投影寻踪的一般概念。分析了基于信息散度指标投影的寻踪方法在高光谱图像处理中的应用，给出了它与主成份分析处理结果的对比。并提出PP与高光谱研究将来的发展方向。     

Grand Tour and Projection Pursuit

Abstract

The grand tour and projection pursuit are two methods for exploring multivariate data. We show how to combine them into a dynamic graphical tool for exploratory data analysis, called a projection pursuit guided tour. This tool assists in clustering data when clusters are oddly shaped and in finding general low-dimensional structure in high-dimensional, and in particular, sparse data. An example shows that the method, which is projection-based, can be quite powerful in situations that may cause grief for methods based on kernel smoothing. The projection pursuit guided tour is also useful for comparing and developing projection pursuit indexes and illustrating some types of asymptotic results.     

Automatic Smoothing Spline Projection Pursuit

Abstract

A highly flexible nonparametric regression model for predicting a response y given covariates {x_k}^d _k=1 is the projection pursuit regression (PPR) model ? = h(x) = β₀ + Σ_jβ_jf_j(α^T _jx) where the f_j , are general smooth functions with mean 0 and norm 1, and Σ^d _k=1α² _kj=1. The standard PPR algorithm of Friedman and Stuetzle (1981) estimates the smooth functions f_j using the supersmoother nonparametric scatterplot smoother. Friedman's algorithm constructs a model with M _max linear combinations, then prunes back to a simpler model of size M ≤ M _max, where M and M _max are specified by the user. This article discusses an alternative algorithm in which the smooth functions are estimated using smoothing splines. The direction coefficients α_j, the amount of smoothing in each direction, and the number of terms M and M _max are determined to optimize a single generalized cross-validation measure.     

Problems of Linear Discrete Games of Pursuit

In this paper, we consider two problems of linear discrete games of pursuit. In each of them, the terms of the sequence defining the pursuer’s control are bounded by some positive number. In the first problem, the terms of the sequence defining the quarry’s control are bounded by some positive number and, in the second problem, the sum of the pth powers of the terms of this sequence is bounded by a given number. For each problem, we obtain a necessary and sufficient condition for the termination of pursuit from all points in space.__________Translated from Matematicheskie Zametki, vol. 77, no. 5, 2005, pp. 707–718.Original Russian Text Copyright ©2005 by G. I. Ibragimov.     

Pursuit problems in linear differential games with delay

In this paper we study linear differential games described by a systemof linear differential equations with delay subject to certain geometric constraints on the control parameters of the players. We obtain sufficient conditions for terminating the pursuit in a finite time.     

苍蝇追逐行为的动态模型

研究苍蝇追逐动态行为,针对不同的追逐行为建立了微分方程模型,利用数值模拟的方法分析模型的渐近行为,并结合微分方程的定性理论,讨论了苍蝇个体大小对追逐动态行为的影响.     

Analysis of Basis Pursuit via Capacity Sets

Finding the sparsest solution α for an under-determined linear system of equations D α=s is of interest in many applications. This problem is known to be NP-hard. Recent work studied conditions on the support size of α that allow its recovery using ? ₁-minimization, via the Basis Pursuit algorithm. These conditions are often relying on a scalar property of D called the mutual-coherence. In this work we introduce an alternative set of features of an arbitrarily given D, called the capacity sets. We show how those could be used to analyze the performance of the basis pursuit, leading to improved bounds and predictions of performance. Both theoretical and numerical methods are presented, all using the capacity values, and shown to lead to improved assessments of the basis pursuit success in finding the sparest solution of D α=s.     

Random Sampling of Sparse Trigonometric Polynomials, II. Orthogonal Matching Pursuit versus Basis Pursuit

We investigate the problem of reconstructing sparse multivariate trigonometric polynomials from few randomly taken samples by Basis Pursuit and greedy algorithms such as Orthogonal Matching Pursuit (OMP) and Thresholding. While recovery by Basis Pursuit has recently been studied by several authors, we provide theoretical results on the success probability of reconstruction via Thresholding and OMP for both a continuous and a discrete probability model for the sampling points. We present numerical experiments, which indicate that usually Basis Pursuit is significantly slower than greedy algorithms, while the recovery rates are very similar.      

High Resolution Pursuit for Feature Extraction

Recently, adaptive approximation techniques have become popular for obtaining parsimonious representations of large classes of signals. These methods include method of frames, matching pursuit, and, most recently, basis pursuit. In this work, high resolution pursuit (HRP) is developed as an alternative to existing function approximation techniques. Existing techniques do not always efficiently yield representations which are sparse and physically interpretable. HRP is an enhanced version of the matching pursuit algorithm and overcomes the shortcomings of the traditional matching pursuit algorithm by emphasizing local fit over global fit at each stage. Further, the HRP algorithm has the same order of complexity as matching pursuit. In this paper, the HRP algorithm is developed and demonstrated on 1D functions. Convergence properties of HRP are also examined. HRP is also suitable for extracting features which may then be used in recognition.     

Projection Pursuit for Exploratory Supervised Classification

In high-dimensional data, one often seeks a few interesting low-dimensional projections that reveal important features of the data. Projection pursuit is a procedure for searching high-dimensional data for interesting low-dimensional projections via the optimization of a criterion function called the projection pursuit index. Very few projection pursuit indices incorporate class or group information in the calculation. Hence, they cannot be adequately applied in supervised classification problems to provide low-dimensional projections revealing class differences in the data. This article introduces new indices derived from linear discriminant analysis that can be used for exploratory supervised classification.     

Equivalence of Linear Programming and Basis Pursuit

In this note, we show that linear programming and the prominent Basis Pursuit problem (i.e., minimizing the ℓ₁-norm of a vector x subject to an underdetermined linear equation system Ax = b) are theoretically equivalent, and briefly discuss possible ramifications regarding computational complexity and practical applicability. (© 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Tools for Two-Dimensional Exploratory Projection Pursuit

Abstract

In this article we examine the current status of exploratory projection pursuit. This involves a large comparison of all two-dimensional projection indexes and optimization algorithms that have been proposed in the literature. We show that the efficacy of the exploration depends to a large extent on the optimization routine. We also stress the importance of studying the behavior of the empirical projection indexes rather than the underlying theoretical distances they estimate. Indeed, indexes based on orthonormal polynomial expansions differ greatly in their behavior from the theoretical performance of the weighted L ²-distances they estimate. This study reveals three universal indexes, namely the Legendre index (Friedman), the Hermite index (Hall), and the chi-squared index (Posse), which are sensitive to any kind of departure from normality in the core of the distribution, and two indexes ideal for catching clusters, namely the Laguerre-Fourier index (Morton) and the Natural Hermite index (Cook, Buja, and Cabrera).