基于自适应的高对比性子空间的高维离群点检测  

Mining outlier in high dimensional space based on adaptive high contrast subspace

作　　者：赵保同[1] 薛安荣[1] 董国宾[1] 

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013

出　　处：《计算机应用研究》2013年第10期2940-2943,共4页Application Research of Computers

基　　金：国家自然科学基金资助项目(60773049);江苏省科技型企业创新资金资助项目(BC2010172);江苏大学高级专业人才科研启动基金项目(09JDG041);高校博士点基金资助项目(20093227110005)

摘　　要：基于子空间解决高维离群点挖掘的问题已经引起人们的广泛关注,现有方法存在的主要问题是难以选取合适的子空间且选取计算量大、阈值等参数设置困难等。这些影响了检测精度和检测效率。利用高对比度子空间选取方法解决子空间选取问题,利用自适应方法解决阈值参数的确定问题,据此提出自适应的高对比性子空间离群点检测方法(AHiCS)。该方法利用统计检验算法选取高对比性子空间,在高对比性的子空间里自适应计算离群点得分,提高了离群点检测的精度与效率。理论和实验表明,该方法可以有效地挖掘高维离群点。Detecting outlier in high dimensional space based on subspace has aroused extensive attention, the main problems that existing methods have are：difficult to select the appropriate subspace and set the threshold parameter,calculation and se- lection cost much time. These affect the detection accuracy and efficiency. This paper used high contrast subspace to solve the problem for selecting subspace, and used adaptive method to solve the threshold parameter, so it proposed the outlier detection method based on adaptive high contrast subspace （AHiCS）. The method used statistical test algorithm of selecting the high con- trast subspace and calculate outlier score under the finding subspace. It improved the accuracy and efficiency of the outlier de- tection. Theoretical and experimental results show that the method can effectively detect outliers.

关 键 词：高维空间 离群点检测 子空间 高对比性 自适应得分 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]