一种基础矩阵线性估计的鲁棒方法

以基础矩阵估计规范八点算法为基础,针对线性算法对参考图像匹配点集中存在的异常匹配极为敏感的问题,将基础矩阵的线性鲁棒估计转换为对匹配点集的最优化采样问题,以最小对极距离平方和中数准则获得基础矩阵的稳定可信解。实验表明,算法对基础矩阵的精度和稳定性均有较大提高。     

实验室间协同试验数据处理软件设计

为降低协同试验数据处理的复杂程度,设计和实现基于开源数据SQLite的协同试验数据处理软件。该软件总体功能包含单元平均值计算、单元离散度计算、曼德尔检验、柯克伦检验、格拉布斯检验、回归方程管理、报告管理以及检验数据管理等。详细分析软件核心功能模块以及SQLite数据库构建方法。经过应用验证,该软件提高了精密度数据处理效率,保障了数据处理的准确性。     

Covariance-guided One-Class Support Vector Machine

In one-class classification, the low variance directions in the training data carry crucial information to build a good model of the target class. Boundary-based methods like One-Class Support Vector Machine (OSVM) preferentially separates the data from outliers along the large variance directions. On the other hand, retaining only the low variance directions can result in sacrificing some initial properties of the original data and is not desirable, specially in case of limited training samples. This paper introduces a Covariance-guided One-Class Support Vector Machine (COSVM) classification method which emphasizes the low variance projectional directions of the training data without compromising any important characteristics. COSVM improves upon the OSVM method by controlling the direction of the separating hyperplane through incorporation of the estimated covariance matrix from the training data. Our proposed method is a convex optimization problem resulting in one global optimum solution which can be solved efficiently with the help of existing numerical methods. The method also keeps the principal structure of the OSVM method intact, and can be implemented easily with the existing OSVM libraries. Comparative experimental results with contemporary one-class classifiers on numerous artificial and benchmark datasets demonstrate that our method results in significantly better classification performance.     

异常点剔除及其并行实现

１．引言 航天技术的发展带来了超大量的数据，如何从这些超大量的数据中获取所需的信息已成为航天技术发展迫切需要解决的关键技术之一． 美国在“阿波罗”登月计划实施登月舱与指挥舱的对接中，就成功地应用了数据处理技术；在海湾战争中，美国实现对伊拉克导弹的拦截中也成功地应用了数据处理技术．这些数据处理系统的实现和应用，依靠的是快速高效的计算机系统和数据处理技术的创新与突破． 战略导弹的研制与定型和载人航天工程需要高精度的外测、遥测、光测测量数据．从多套、多站、多种测量设备的联合测量数据中摸清误差规律、调整参…     

Partial least squares with outlier detection in spectral analysis: A tool to predict gasoline properties

The aim of this study is to propose a novel partial least squares with outlier detection (PLS_OD) calibration method and show its usefulness in calibration successfully with data containing outlying objects. We apply this method in gasoline spectral analysis to predict gasoline properties. In particular, a comparative study of PLS_OD and other five methods is presented. The performances of the proposed method are illustrated on spectral data set with and without outliers. The obtained results suggest that the proposed method can be used for constructing satisfactory gasoline prediction model whether there are some outliers or not.     

Outlier detection for simple default theories

It was noted recently that the framework of default logics can be exploited for detecting outliers. Outliers are observations expressed by sets of literals that feature unexpected properties. These observations are not explicitly provided in input (as it happens with abduction) but, rather, they are hidden in the given knowledge base. Unfortunately, in the two related formalisms for specifying defaults — Reiter's default logic and extended disjunctive logic programs — the most general outlier detection problems turn out to lie at the third level of the polynomial hierarchy. In this note, we analyze the complexity of outlier detection for two very simple classes of default theories, namely NU and DNU, for which the entailment problem is solvable in polynomial time. We show that, for these classes, checking for the existence of an outlier is anyway intractable. This result contributes to further showing the inherent intractability of outlier detection in default reasoning.     

数据挖掘技术在构造范例库中的应用

将基于范例的推理应用于实际系统中，一个很关键的问题是如何构造一个丰富而高效的范例库。采用了数据挖掘技术， 提出了一个综合算法，从传统数据库中构造范例库，以提高知识获的自动化程度。     

Registration of partially overlapping surfaces by rejection of false point correspondences

We present a new algorithm for the registration of three-dimensional partially overlapping surfaces. It is based on an efficient scheme for the rejection of false point correspondences (correspondence outliers) and does not require initial pose estimation or feature extraction. An initial list of corresponding points is first derived using the regional properties of vertices on both surfaces. From these point correspondences, pairs of corresponding rigid triplets are formed. The normal vectors at the vertices of each corresponding triplet are used to compute the candidate rotations. By clustering the candidate rotation axes and candidate rotation angles separately, a large number of false correspondences are eliminated and an approximate rotation is decided, from which an approximate translation is also obtained. Finally, the optimal transformation parameters are determined by further refining the estimated parameters in an iterative manner. Mathematical analysis and experimental results show that the registration process is fast and accurate even when the objects are regularly shaped and contain many regionally similar surface patches.     

Robust locally linear embedding

In the past few years, some nonlinear dimensionality reduction (NLDR) or nonlinear manifold learning methods have aroused a great deal of interest in the machine learning community. These methods are promising in that they can automatically discover the low-dimensional nonlinear manifold in a high-dimensional data space and then embed the data points into a low-dimensional embedding space, using tractable linear algebraic techniques that are easy to implement and are not prone to local minima. Despite their appealing properties, these NLDR methods are not robust against outliers in the data, yet so far very little has been done to address the robustness problem. In this paper, we address this problem in the context of an NLDR method called locally linear embedding (LLE). Based on robust estimation techniques, we propose an approach to make LLE more robust. We refer to this approach as robust locally linear embedding (RLLE). We also present several specific methods for realizing this general RLLE approach. Experimental results on both synthetic and real-world data show that RLLE is very robust against outliers.     

Extracting lines of curvature from noisy point clouds

We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.