基于稀疏子空间的局部异常值检测算法  

Sparse Subspace-Based Method for Local Outlier Detection

作　　者：覃凤婷 杨有龙 仇海全 QIN Fengting;YANG Youlong;QIU Haiquan(School of Mathematics and Statistics,Xidian University,Xi’an 710126,China;College of Information and Network Engineering,Anhui Science and Technology University,Fengyang,Anhui 233100,China)

机构地区：[1]西安电子科技大学数学与统计学院,西安710126 [2]安徽科技学院信息与网络工程学院,安徽凤阳233100

出　　处：《计算机工程与应用》2020年第19期152-159,共8页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61573266);安徽省高校自然科学研究重点项目(No.KJ2019A0816)。

摘　　要：针对高维数据集中存在不相关的属性与冗余数据导致无法检测出异常值的问题,提出了一种新的基于稀疏子空间的局部异常值检测算法(SSLOD)。根据数据对象在每个维度上的局部密度定义了对象的异常因子;依据异常因子阈值约简数据集中与局部异常值不相关的属性以及冗余的数据对象;用改进的粒子群优化算法在约简后的数据集中搜索稀疏子空间,该子空间中的数据对象即为异常值。通过在仿真数据集和真实数据集上的综合实验验证了该算法的有效性和准确性。Aiming at the problem that outliers cannot be detected due to irrelevant attributes and redundant data in the high-dimensional data sets, a new Sparse Subspace-based algorithm for Local Outlier Detection(SSLOD)is proposed.Firstly, the outlier factor of the object is defined according to the local density of the object in each dimension. Secondly,the attributes unrelated to local outliers and redundant objects in the data set are reduced based on the threshold of outlier factor. Finally, the improved particle swarm optimization algorithm is used to search sparse subspace in the simplified data set, and the local outliers are included in the sparse subspace. The effectiveness and accuracy of the proposed algorithm is demonstrated by the comprehensive experiments on synthetic and real-life data sets.

关 键 词：异常值检测 数据约简 粒子群算法 稀疏子空间 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]