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Multi-view Outlier Detection

  • Zhengming Ding
  • Handong Zhao
  • Yun Fu
Chapter
Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Abstract

Identifying different types of multi-view data outliers with abnormal behaviors is an interesting yet challenging unsupervised learning task, due to the complicated data distributions across different views. Conventional approaches achieve this by learning a new latent feature representation with the pairwise constraint on different view data. We argue that the existing methods are expensive in generalizing their models from two-view data to three-view (or more) data, in terms of the number of introduced variables and detection performance. In this chapter, we propose a novel multi-view outlier detection method with a consensus regularization on the latent representations.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Indiana University-Purdue University IndianapolisIndianapolisUSA
  2. 2.Adobe ResearchSan JoseUSA
  3. 3.Northeastern UniversityBostonUSA

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