Computing the least quartile difference estimator in the plane

A common problem in linear regression is that largely aberrant values can strongly influence the results. The least quartile difference (LQD) regression estimator is highly robust, since it can resist up to almost 50% largely deviant data values without becoming extremely biased. Additionally, it shows good behavior on Gaussian data—in contrast to many other robust regression methods. However, the LQD is not widely used yet due to the high computational effort needed when using common algorithms. It is shown that it is possible to compute the LQD estimator for n bivariate data points in expected running time O(n²logn) or deterministic running time . Additionally, two easy to implement algorithms with slightly inferior time bounds are presented. All of these algorithms are also applicable to least quantile of squares and least median of squares regression through the origin, improving the known time bounds to expected time O(nlogn) and deterministic time . The proposed algorithms improve on known results of existing LQD algorithms and hence increase the practical relevance of the LQD estimator.     

An extension on robust directed projection of points onto point clouds

Azariadis and Sapidis [Azariadis PN, Sapidis NS. Drawing curves onto a cloud of points for point-based modelling. Computer-Aided Design 2005;37(1):109-22] introduced a novel method of point directed projection (DP) onto a point cloud along an associated projection vector. This method is essentially based on an idea of least sum of squares by making use of a weight function for bounding the influence of noise. One problem with their method is the lack of robustness for outliers. Here, we present a simple, robust, and efficient algorithm: robust directed projection (RDP) to guide the DP computation. Our algorithm is based on a robust statistical method for outlier detection: least median of squares (LMS). In order to effectively approximate the LMS optimization, the forward search technique is utilized. The algorithm presented here is better suited to detect outliers than the DP approach and thus finds better projection points onto the point cloud. One of the advantages of our algorithm is that it automatically ignores outliers during the directed projection phase.     

Robust principal axes determination for point-based shapes using least median of squares

A robust technique for determining the principal axes of a 3D shape represented by a point set, possibly with noise, is presented. We use techniques from robust statistics to guide the classical principal component analysis (PCA) computation. Our algorithm is based on a robust statistics method: least median of squares (LMS), for outlier detection. Using this method, an outlier-free major region of the shape is extracted, which ignores the effect on other minor regions regarded as the outliers of the shape.In order to effectively approximate the LMS optimization, the forward search technique is utilized. We start from a small outlier-free subset robustly chosen as the major region, where an octree is used for accelerating computation. Then the region is iteratively increased by adding samples at a time. Finally, by treating the points on minor regions as outliers, we are able to define the principal axes of the shape as one of the major region. One of the advantages of our algorithm is that it automatically disregards outliers and distinguishes the shape as the major and minor regions during the principal axes determination without any extra segmentation procedure. The presented algorithm is simple and effective and gives good results for point-based shapes. The application on shape alignment is considered for demonstration purpose.     

Robust fuzzy regression analysis

In this paper we propose a robust fuzzy linear regression model based on the Least Median Squares-Weighted Least Squares (LMS-WLS) estimation procedure. The proposed model is general enough to deal with data contaminated by outliers due to measurement errors or extracted from highly skewed or heavy tailed distributions. We also define suitable goodness of fit indices useful to evaluate the performances of the proposed model. The effectiveness of our model in reducing the outliers influence is shown by using applicative examples, based both on simulated and real data, and by a simulation study.     

Bayesian identification of clustered outliers in multiple regression

We propose a Bayesian model for clustered outliers in multiple regression. In the literature, outliers are frequently modeled as coming from a subgroup where the variance of the errors is much larger than in the rest of the data. By contrast, when a cluster of outliers exists, we show that it can be more informative to model them as coming from a subgroup where different regression coefficients hold. We can explicitly model the clustering phenomenon by assuming that the probability of an outlier is a function of the explanatory variables. Fitting proceeds via the Gibbs sampler, using the Metropolis-Hastings algorithm to produce variates from the more unusual distributions. Initialization uses a least median of squares fit, and in some ways this method can be viewed as a Bayesian version of the many algorithms that use this fit as a start to some more efficient estimator. This method works very well in a variety of test data sets. We illustrate its use in a data set of sailboat prices, where it yields information both on the identity of the outliers and on their location, spread, and the regression coefficients inside the minority subgroup.     

A parallel double-level multiobjective evolutionary algorithm for robust optimization

Robust optimization is a popular method to tackle uncertain optimization problems. However, traditional robust optimization can only find a single solution in one run which is not flexible enough for decision-makers to select a satisfying solution according to their preferences. Besides, traditional robust optimization often takes a large number of Monte Carlo simulations to get a numeric solution, which is quite time-consuming. To address these problems, this paper proposes a parallel double-level multiobjective evolutionary algorithm (PDL-MOEA). In PDL-MOEA, a single-objective uncertain optimization problem is translated into a bi-objective one by conserving the expectation and the variance as two objectives, so that the algorithm can provide decision-makers with a group of solutions with different stabilities. Further, a parallel evolutionary mechanism based on message passing interface (MPI) is proposed to parallel the algorithm. The parallel mechanism adopts a double-level design, i.e., global level and sub-problem level. The global level acts as a master, which maintains the global population information. At the sub-problem level, the optimization problem is decomposed into a set of sub-problems which can be solved in parallel, thus reducing the computation time. Experimental results show that PDL-MOEA generally outperforms several state-of-the-art serial/parallel MOEAs in terms of accuracy, efficiency, and scalability.     

一种改进的在线最小二乘支持向量机回归算法

针对一般最小二乘支持向量机处理大规模数据集会出现训练速度幔、计算量大、不易在线训练的缺点,将修正后的遗忘因子矩形窗方法与支持向量机相结合,提出一种基于改进的遗忘因子矩形窗算法的在线最小二乘支持向量机回归算法,既突出了当前窗口数据的作用,又考虑了历史数据的影响.所提出的算法可减少计算量,提高在线辨识精度.仿真算例表明了该方法的有效性.     

Fast robust regression algorithms for problems with Toeplitz structure

The problem of computing an approximate solution of an overdetermined system of linear equations is considered. The usual approach to the problem is least squares, in which the 2-norm of the residual is minimized. This produces the minimum variance unbiased estimator of the solution when the errors in the observations are independent and normally distributed with mean 0 and constant variance. It is well known, however, that the least squares solution is not robust if outliers occur, i.e., if some of the observations are contaminated by large error. In this case, alternate approaches have been proposed which judge the size of the residual in a way that is less sensitive to these components. These include the Huber M-function, the Talwar function, the logistic function, the Fair function, and the ?₁ norm. New algorithms are proposed to compute the solution to these problems efficiently, in particular, when the matrix A has small displacement rank. Matrices with small displacement rank include matrices that are Toeplitz, block-Toeplitz, block-Toeplitz with Toeplitz blocks, Toeplitz plus Hankel, and a variety of other forms. For exposition, only Toeplitz matrices are considered, but the ideas apply to all matrices with small displacement rank. Algorithms are also presented to compute the solution efficiently when a regularization term is included to handle the case when the matrix of the coefficients is ill-conditioned or rank-deficient. The techniques are illustrated on a problem of FIR system identification.     

视频序列中消除异常点的SRR鲁棒算法

提高超分辨率图像重建效果一个重要因素是减小数据“异常点”的影响。介绍了LMS算法在超分辨率图像重建中的应用,在这种算法的静态模型基础上,提出了一种重建视频图像序列过程中消除“异常点”影响的方法。在考虑配准误差的条件下,这种方法可以适用于实际应用中的瞬态和稳态相位的图像。     

基于AdaBoost算法的加权二乘向量回归机

针对二乘向量机(LS-SVM)对所有样本误差惩罚相同、预测精度不高的问题,提出了一种基于AdaBoost模型的二乘向量回归机。该算法使用多个二乘向量机按照某种学习规则协调各二乘向量机的输出,同时根据回归精度,建立各二乘向量机中每一个样本的误差惩罚权重,以突出样本的惩罚差异性,提高算法的泛化性能。实验结果表明,提出的算法提高了二乘向量回归机的预测精度,优化了学习机的性能。     

回归支持向量机SMO算法的改进

在Smola 和Sch?觟lkopf的SMO算法中,由于使用了单一的极限值而使得算法的效果没有完全表现出来。使用KKT条件来检验二次规划问题,使用两个极限参量来对回归SMO算法进行改进。通过对比实验,这一改进算法在执行速度上表现出了非常好的性能。     

数字信号调制识别的RBFNN分类器算法研究与实现

提出了一种新的用于数字信号调制识别的径向基函数神经网络（RBFNN）分类器算法。该算法采用减法聚类算法和最小均方算法实现了对隐含层中心点个数及位置和输出层权值系数的自适应训练。此算法能够综合考虑所有特征参量,能够在多维空间内找到最佳分界面;同时,解决了隐含层中心点个数及位置的盲目性和随机性的问题。仿真实验表明,在相同特征参量情况下,该算法能够有效提高正确识别率。     

基于改进LMS算法的TH-UWB接收机

跳时超宽带（TH-UWB）无线通信系统通常采用RAKE接收技术。而信道估计的准确度直接影响系统接收性能。提出一种基于梯度的变步长的LMS算法进行信道估计。与传统LMS算法相比,改进的LMS算法可以获得更小的MSE（Mean Square Error）从而为接收机提供更精确的信道估计量。同时,结果也表明该算法提高了整个接收机性能,并获得更小的BER（Bit Error Rate）。     

Robust estimation of BRDF model parameters

The effect of the Bidirectional Reflectance Distribution Function (BRDF) is one of the most important factors in correcting the reflectance obtained from remotely sensed data. Estimation of BRDF model parameters can be deteriorated by various factors; contamination of the observations by undetected subresolution clouds or snow patches, inconsistent atmospheric correction in multiangular time series due to uncertainties in the atmospheric parameters, slight variations of the surface condition during a period of observation, for example due to soil moisture changes, diurnal effects on vegetation structure, and geolocation errors [Lucht and Roujean, 2000]. In the present paper, parameter estimation robustness is examined using Bidirectional Reflectance Factor (BRF) data measured for paddy fields in Japan. We compare both the M-estimator and the least median of squares (LMedS) methods for robust parameter estimation to the ordinary least squares method (LSM). In experiments, simulated data that were produced by adding noises to the data measured on the ground surface were used. Experimental results demonstrate that if a robust estimation is sought, the LMedS method can be adopted for the robust estimation of a BRDF model parameter.     

基于加速鲁棒特征的广角图像自动拼接校正算法

广角图像在全景图拼接中优势明显,然而,由于广角图像存在严重的透视畸变,往往导致拼接的效果不理想,甚至导致拼接失败。针对广角图像拼接中的透视畸变问题,提出一种广角图像自动拼接、校正算法。先计算出待拼接广角图像的特征点匹配对,求出图像拼接矩阵,对广角图像进行拼接;然后,根据图像采集时的镜头旋转角度计算出图像校正矩阵,对拼接后的全景图进行透视畸变校正。实验结果表明,基于该算法得到的全景图能够有效地消除透视畸变,达到较好的拼接效果。     

An efficient VLSI architecture for fast motion estimation by hybridizing parallel spiral and adaptive threshold star diamond search algorithm-based search algorithm in HEVC

This article proposes a high efficiency video coding (HEVC) standard hardware using block matching motion estimation algorithm. A hybrid parallel spiral and adaptive threshold star diamond search algorithm (Hyb PS-ATSDSA) proposes for fast motion estimation in HEVC. Parallel spiral search approach utilizes spiral pattern for searching from center to the surroundings and adaptive threshold SDA consists of two phases, they are adaptive threshold and star diamond algorithm. To lower computational complexities in HEVC architecture, parallel spiral search algorithm uses several blocks matching schemes. Adaptive threshold and star diamond algorithm are used to reduce the matching errors and remove the invalid blocks early from the procedure of motion estimation and finally predicts the final motion of the image. Speed is increased while using this hybrid algorithm. Proposed structure is carried out in Xilinx; ISE 14.5 design suit, then the experimental outcomes are analyzed to existing motion estimation strategies in field-programmable gate array devices. Experimental performance of the proposed Hyb-PS-ATSDSA-ME-HEVC method attains lower delay 33.97%, 32.97%, 62.97, and 26.97%, and lower area 34.867%, 45.97%, 27.97%, and 43.967% compared with the existing methods, such as FSA-ME-HEVC, TZSA-ME-HEVC, hyb TZSA-IME-HEVC, and IBMSA-ME-HEVC, respectively.     

优化网络入侵特征库的量子进化算法

针对网络入侵检测系统中入侵特征库的性能普遍较差的缺点,提出了一种优化网络入侵特征库的改进量子进化算法（IQEA）。采用特征向量表示染色体结构,借鉴小生境协同进化思想初始化种群,以个体的匹配程度设计适应度函数,使用动态更新和“优体交叉”策略进化种群。仿真实验表明,IQEA的寻优能力和收敛速度均优于量子进化算法和进化算法,经IQEA优化后的入侵特征库,检测能力强,并具有较好的自适应性。     

一种改进的声测定位时延估计算法

研究了时延估计算法在被动声测定位中的应用,提出了一种改进的基于最大似然(ML)权函数的广义互相关时延估计算法。改进的算法采用加窗法和最小均方差（LMS)滤波法,弥补了原算法计算量大及无法消除回响干扰的不足。仿真结果表明,改进的算法计算复杂度明显降低,能够有效地消除回响干扰,具有较高的时延估计精度和鲁棒性。     

基于改进支持向量回归算法的移动机器人定位

为了提高移动机器人定位精度,提出了一种基于正交编码器和陀螺仪的轮式移动机器人定位系统,建立机器人的定位模型和运动学模型。研究了支持向量回归（SVR）算法,为获得更好的鲁棒性,对目标函数误差平方进行加权,分析不同参数优化算法对支持向量机回归准确率的影响。以自制的移动机器人为实验平台,将改进的算法与最小二乘支持向量回归（LSSVR）算法、加权最小二乘支持向量回归（WLSSVR）算法进行比较,对比了用改进算法时机器人在木地板场地与瓷砖场地的定位误差情况,并对正交编码器+陀螺仪定位系统与双码盘定位系统、单码盘+陀螺仪定位系统进行比较。实验结果表明,改进的算法使机器人的定位精度明显高于对比算法,并且所提出的定位系统定位效果较好。     

RFID系统中多电子标签防碰撞改进算法

在现有防碰撞算法的基础上提出了一种改进的二进制搜索算法。当读写器检测到碰撞位之后,仅需要记录最高碰撞位和次高碰撞位的位置,并设定这两个位置上的比特数作为下次查询命令,从而使系统的传输数据量、查询次数及传输时间大大减少,提高了系统的吞吐率。仿真结果表明,改进后的算法比二进制搜索算法和动态二进制搜索算法更具优势。