高阶多视图离群点检测  

High-order Multi-view Outlier Detection

作　　者：钟颖宇 陈松灿[1] ZHONG Ying-yu;CHEN Song-can(College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院,南京211106

出　　处：《计算机科学》2020年第9期99-104,共6页Computer Science

基　　金：国家自然科学基金重点项目(61732006)。

摘　　要：由于数据在不同视图之间的分布比较复杂,传统的单视图离群点检测方法不再适用于多视图离群点的检测,使得多视图离群点检测成为一个颇具挑战性的研究课题。多视图离群点可分为3种类型:属性离群点、类离群点和类-属性离群点。现有方法采用跨视图成对约束来学习新的特征表示,并根据这些特征来定义离群点评分度量。这些方法没有充分利用视图间的交互信息,并且在面对3个或更多视图时会导致计算的复杂度更高。为此,文中考虑将多视图数据重塑成张量集形式,定义高阶多视图离群点,并且证明现有的三类多视图离群点都满足高阶多视图离群点的定义,从而提出一种新的多视图离群点检测算法——高阶多视图离群点检测算法(High-Order Multi-View Outlier Detection,HOMVOD)。该算法首先将多视图数据重塑成张量集形式,然后学习其低秩表示,最后设计张量表示下的离群值函数来实现检测。在UCI数据集上的实验表明,HOMVOD算法在检测多视图离群点方面优于现有方法。Due to the complex distribution of data between different views,the traditional single-view outlier detection method is no longer applicable to the detection of multi-view outliers,making multi-view outlier detection a challenging research topic.Multi-view outliers can be divided into three types:attribute outliers,class outliers,and class-attribute outliers.Existing methods use pairwise constraints across views to learn new feature representations and define outlier scoring metrics based on these features,which do not take full advantage of the interactive information between views and results in higher computational complexity when facing three or more views.Therefore,this paper considers to reshape multi-view data into tensor set form,defines high-order multi-view outliers,and proves that all of the existing three types of multi-view outliers meet the definition of high-order multi-view outliers,so as to propose a new multi-view outliers detection algorithm called high-order multi-view outliers detection algorithm(HOMVOD).Specifically,the algorithm firstly reshapes multi-view data into tensor set form,then learns its low-rank representation,and finally designs outlier function under tensor representation to realize detection.Experiments on UCI datasets show that this method is superior to existing methods in detecting multi-view outliers.

关 键 词：多视图离群点检测 多视图学习 异常检测 张量表示 低秩表示 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]