计算机科学 ›› 2020, Vol. 47 ›› Issue (9): 99-104.doi: 10.11896/jsjkx.200600170
钟颖宇, 陈松灿
ZHONG Ying-yu, CHEN Song-can
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。
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