隐目标回归算法设计研究

对常用的回归方法进行研究.此类方法虽然几何解释明确、易于求解,但均须事先确定(或假定)变量间的因果关系,再考虑建模,在实际应用中,对于很难确定变量的因果关系的问题,如物联网数据分析,上述方法就会失效.为此,提出一种无需假定因变量的隐目标回归方法.该方法易于核化,可以推广到非线性回归问题.通过人工数据和国际标准数据集上的实验验证了所提算法的有效性.     

LDE融合光谱回归分类的光照变化人脸识别

针对光照变化人脸识别问题中传统的光谱回归算法不能很好地进行特征提取而严重影响识别性能的问题,提出了局部判别嵌入优化光谱回归分类的人脸识别算法。计算出训练样本的特征向量;借助于数据的近邻和分类关系,利用局部判别嵌入算法构建分类问题所需的嵌入,同时学习每种分类的子流形所需的嵌入;利用光谱回归分类算法计算投影矩阵,并利用最近邻分类器完成人脸的识别。在两大人脸数据库扩展YaleB及CMU PIE上的实验验证了该算法的有效性,实验结果表明,相比其他光谱回归算法,该算法取得了更高的识别率、更好的工作特性,并且降低了计算复杂度。     

FAST LINEAR ESTIMATION METHODS FOR VECTOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

Abstract. Three linear methods for estimating parameter values of vector auto-regressive moving-average (VARMA) models which are in general at least an order of magnitude faster than maximum likelihood estimation are developed in this paper. Simulation results for different model structures with varying numbers of component series and observations suggest that the accuracy of these procedures is in most cases comparable with maximum likelihood estimation. Procedures for estimating parameter standard error are also discussed and used for identification of nonzero elements in the VARMA polynomial structures. These methods can also be used to establish the order of the VARMA structure. We note, however, that the primary purpose of these estimates is to generate initial estimates for the nonzero parameters in order to reduce subsequent computational time of more efficient estimation procedures such as exact maximum likelihood.     

基于GM（1，1）和线性回归模型在印刷包衬压缩变形中的数据预测研究

运用线性回归对预测数据进行分析,剔除异常数据,用GM（1,1）模型进行预测,有效降低了数据相对误差,提高了预测数据的精度。选用印刷包衬压缩变形的压缩变形量值,用线性回归进行数据分析并剔除异常数据后用GM（1,1）进行预测,使得预测数据具有更高的准确性和适应性。实验及仿真结果表明,经过前期数据分析整理后的灰色预测模型,其预测期望值远优于单纯的回归模型和GM（1,1）模型。     

基于交互式逐步回归的双语教学态度预测模型

针对多元线性回归分析法预测双语教学态度误差较大的问题,通过引入变量的平方项及交叉乘积项建立拟线性回归方程,利用交互式逐步回归分析法对变量进行筛选,并以筛选后的变量建模。将调查数据分为建模样本数据和测试样本数据,测试结果表明经过变量扩维及筛选所建立的预测模型精确度有较大提高,为语言学研究提供了一种新方法,值得推扩和借鉴。     

An efficient approach for face recognition based on common eigenvalues

In this paper, a simple technique is proposed for face recognition among many human faces. It is based on the polynomial coefficients, covariance matrix and algorithm on common eigenvalues. The main advantage of the proposed approach is that the identification of similarity between human faces is carried out without computing actual eigenvalues and eigenvectors. A symmetric matrix is calculated using the polynomial coefficients-based companion matrices of two compared images. The nullity of a calculated symmetric matrix is used as similarity measure for face recognition. The value of nullity is very small for dissimilar images and distinctly large for similar face images. The feasibility of the propose approach is demonstrated on three face databases, i.e., the ORL database, the Yale database B and the FERET database. Experimental results have shown the effectiveness of the proposed approach for feature extraction and classification of the face images having large variation in pose and illumination.     

Adaptive block size for dense QR factorization in hybrid CPU–GPU systems via statistical modeling

QR factorization is a computational kernel of scientific computing. How can the latest computer be used to accelerate this task? We investigate this topic by proposing a dense QR factorization algorithm with adaptive block sizes on a hybrid system that contains a central processing unit (CPU) and a graphic processing unit (GPU). To maximize the use of CPU and GPU, we develop an adaptive scheme that chooses block size at each iteration. The decision is based on statistical surrogate models of performance and an online monitor, which avoids unexpected occasional performance drops. We modify the highly optimized CPU–GPU based QR factorization in MAGMA to implement the proposed schemes. Numerical results suggest that our approaches are efficient and can lead to near-optimal block sizes. The proposed algorithm can be extended to other one-sided factorizations, such as LU and Cholesky factorizations.     

Application of a rice field experimental error distribution function to nitrogen-phosphorus-potassium fertilizer response model analysis

A distribution function of rice yield deviations from the mean was developed from field experiments with 555 plots at 16 sites in Zhejiang province, China, for three years. The deviation distribution in interval of 50kg/ha appeared as a symmetrical distribution with a high peak (Mean=0.279 [kg/ha], SD=240.686 [kg/ha]). Normality test using Kolmogrove-Smirnov test between the observed cumulative distribution and the normal cumulative distribution function indicates that the observed deviation distribution is not normal. An empirical exponential cumulative distribution function was developed. The distribution function was used to remove outliers during the development of a rice yield fertilizer response model, based on data from a non-replicated NPK field experiment.     

昭通横江流域年径流预测

针对昭通横江由南到北贯穿昭通全境,水资源量丰富,但南北差异大,南部少、北部多的特点,根据流域内水文站实测年径流、大气环流指数、海温指数及其他指数等资料系列,采用多元回归分析法拟合1981—2010年径流,建立了预测模型,验证、预测分析了2011—2016年径流量。结果表明:干流控制站预测精度高于干流上段及支流代表站,水利工程建设等人类活动影响是导致出现这样结果的主要原因;多元回归分析在横江流域径流预测中具有一定的适应性,但因指标的差异及相关参数可获取性的差异,预测结果存在区域间、等级间的差别。     

Real-time facial animation on mobile devices

We present a performance-based facial animation system capable of running on mobile devices at real-time frame rates. A key component of our system is a novel regression algorithm that accurately infers the facial motion parameters from 2D video frames of an ordinary web camera. Compared with the state-of-the-art facial shape regression algorithm [1], which takes a two-step procedure to track facial animations (i.e., first regressing the 3D positions of facial landmarks, and then computing the head poses and expression coefficients), we directly regress the head poses and expression coefficients. This one-step approach greatly reduces the dimension of the regression target and significantly improves the tracking performance while preserving the tracking accuracy. We further propose to collect the training images of the user under different lighting environments, and make use of the data to learn a user-specific regressor, which can robustly handle lighting changes that frequently occur when using mobile devices.