Computer Science ›› 2020, Vol. 47 ›› Issue (9): 99-104.doi: 10.11896/jsjkx.200600170

• Database & Big Data & Data Science • Previous Articles     Next Articles

High-order Multi-view Outlier Detection

ZHONG Ying-yu, CHEN Song-can   

  1. College of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Received:2020-06-28 Published:2020-09-10
  • About author:ZHONG Ying-yu,born in 1995,postgraduate.His main research interests include multi-view learning and anomaly detection.
    CHEN Song-can,born in 1962,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include pattern recognition,machine learning and neural computing.
  • Supported by:
    Key Program of National Natural Science Foundation of China (61732006).

Abstract: Due to the complex distribution of data between different views,the traditional single-view outlier detection method is no longer applicable to the detection of multi-view outliers,making multi-view outlier detection a challenging research topic.Multi-view outliers can be divided into three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.

Key words: Anomaly detection, Low-rank representation, Multi-view learning, Multi-view outlier detection, Tensor representation

CLC Number: 

  • TP181
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