Robustified least squares support vector classification

Support vector machine (SVM) algorithms are a popular class of techniques to perform classification. However, outliers in the data can result in bad global misclassification percentages. In this paper, we propose a method to identify such outliers in the SVM framework. A specific robust classification algorithm is proposed adjusting the least squares SVM (LS‐SVM). This yields better classification performance for heavily tailed data and data containing outliers. Copyright © 2009 John Wiley & Sons, Ltd.     

数据离群值的检验及处理方法讨论

由目前普遍使用的分析化学教材中与离群值相关的内容出发,对其进行对比和分析,说明数据离群值检验的意义和重要性。对几种常见的离群值判定方法进行了列举和比较,并对已确认为异常值的数据的处理方法也进行了对比和讨论。各种检验和处理方法各有其优势和劣势,根据实际情况选择不同方法或多种方法配合使用,能够更好地实现对离群值的检验和处理。     

Robust Estimation for Bivariate Poisson INGARCH Models

In the integer-valued generalized autoregressive conditional heteroscedastic (INGARCH) models, parameter estimation is conventionally based on the conditional maximum likelihood estimator (CMLE). However, because the CMLE is sensitive to outliers, we consider a robust estimation method for bivariate Poisson INGARCH models while using the minimum density power divergence estimator. We demonstrate the proposed estimator is consistent and asymptotically normal under certain regularity conditions. Monte Carlo simulations are conducted to evaluate the performance of the estimator in the presence of outliers. Finally, a real data analysis using monthly count series of crimes in New South Wales and an artificial data example are provided as an illustration.     

Use of cluster separation indices and the influence of outliers: application of two new separation indices,the modified silhouette index and the overlap coefficient to simulated data and mouse urine metabolomic profiles

To quantify separate classes, four indices are compared namely the Davies Bouldin index, the silhouette width and two new approaches described in this paper, the modified silhouette width index based on the proportion of objects with a positive silhouette width and the Overlap Coefficient. Four sets of simulated datasets are described, each in turn, consisting of 15 sets of data of varying degrees of overlap, and differing in the nature of outliers. Three experimental datasets consisting of the gas chromatography mass spectrometry of extracts from mouse urine obtained to study the effect of different environmental (stress), physiological (diet) and developmental (age) factors on their metabolic profiles are also described. The paper discusses the robustness of each approach to outliers, and to allow assessment of class separation for each index. The two modifications protect against outliers. Copyright © 2008 John Wiley & Sons, Ltd.     

A fully robust PARAFAC method for analyzing fluorescence data

Parallel factor analysis (PARAFAC) is a widespread method for modeling fluorescence data by means of an alternating least squares procedure. Consequently, the PARAFAC estimates are highly influenced by outlying excitation–emission landscapes (EEM) and element‐wise outliers, like for example Raman and Rayleigh scatter. Recently, a robust PARAFAC method that circumvents the harmful effects of outlying samples has been developed. For removing the scatter effects on the final PARAFAC model, different techniques exist. Newly, an automated scatter identification tool has been constructed. However, there still exists no robust method for handling fluorescence data encountering both outlying EEM landscapes and scatter. In this paper, we present an iterative algorithm where the robust PARAFAC method and the scatter identification tool are alternately performed. A fully automated robust PARAFAC method is obtained in that way. The method is assessed by means of simulations and a laboratory‐made data set. Copyright © 2009 John Wiley & Sons, Ltd.     

A new strategy of outlier detection for QSAR/QSPR

The crucial step of building a high performance QSAR/QSPR model is the detection of outliers in the model. Detecting outliers in a multivariate point cloud is not trivial, especially when several outliers coexist in the model. The classical identification methods do not always identify them, because they are based on the sample mean and covariance matrix influenced by the outliers. Moreover, existing methods only lay stress on some type of outliers but not all the outliers. To avoid these problems and detect all kinds of outliers simultaneously, we provide a new strategy based on Monte‐Carlo cross‐validation, which was termed as the MC method. The MC method inherently provides a feasible way to detect different kinds of outliers by establishment of many cross‐predictive models. With the help of the distribution of predictive residuals such obtained, it seems to be able to reduce the risk caused by the masking effect. In addition, a new display is proposed, in which the absolute values of mean value of predictive residuals are plotted versus standard deviations of predictive residuals. The plot divides the data into normal samples, y direction outliers and X direction outliers. Several examples are used to demonstrate the detection ability of MC method through the comparison of different diagnostic methods. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010     

多维统计数据质量检验与异常点识别的模型与方法

本文证明 ,反映社会经济规模大小的多维统计数据近似服从多维对数正态分布 .基于此 ,本文提出了对这类数据的质量及其异常点进行检查和识别的多维对数正态分布检验法 ,并以我国中央部门属研究机构的调查数据为例进行了研究     

Outliers in SAR and QSAR: 2. Is a flexible binding site a possible source of outliers?

Structure-activity relationship (SAR) and/or quantitative structure-activity relationship (QSAR) studies play an important role in a lead optimization of drug discovery research. When there is a lack of ligand-bound protein structural information, one of the assumptions in SAR and QSAR studies is that similar analogs bind to the same binding site in a similar binding mode. In such studies, outliers have often been observed, especially in QSAR. However, most of these studies have focused their attention on the development of QSAR and left outliers unattended. We searched ligand-bound X-ray crystal structures from the protein structure database to find evidences that could indicate a possible source of outliers in SAR or QSAR. Our results showed the possibility of conformational changes in a flexible binding site as one possible source of outliers. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Detecting multiple outliers in linear regression using a cluster method combined with graphical visualization

This paper provides a graphical visualization of multiple outliers based on a clustering algorithm using the minimal spanning tree, and proposes a modified version of this clustering algorithm for identifying multiple outliers. Graphical visualization is helpful for the classification of multiple outliers. It is shown that the proposed modified procedure preserves the performance of the clustering algorithm in identifying multiple outliers, but also reduces the problem of swamping of observations.     

近邻点一致性的随机抽样一致性算法

 随机抽样一致性算法是计算机视觉领域中应用广泛的多视几何图像参数估计方法之一。为提高在高噪声条件下原算法计算效率,提出基于匹配点对邻近点集合一致性的过滤方法,可提高原初始匹配点对集合的内点比例,减少使用RANSAC时寻找参数时的抽样次数。宽基线图像极线几何计算的多组实验表明：提出的算法能够保持与原RANSAC一致的结果,并有效地提高计算速度。