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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

4. Multi-view Outlier Detection

verfasst von : Zhengming Ding, Handong Zhao, Yun Fu

Erschienen in: Learning Representation for Multi-View Data Analysis

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Multi-view Clustering with Partial Information
Nächstes Kapitel Multi-view Transformation Learning
Fußnoten
1
Key notations are tabulated in Table 4.1 for reference.
 
7
We also evaluate on two more UCI datasets, including Credit-card Clients and KDD Cup 1999. The results can be found in the supplementary material.
 
8
For the sensitivity analysis on different split strategies, we conduct analytical experiments on both 2-view and 3-view cases, which can be found in the supplementary material.
 
9
The results on datasets credit-card and kdd-cup with 4-view and 5-view splits can be found in the supplementary material.
 
10
In some computer vision articles (Lee et al. 2011), these object-like regions are also called “proposals”.
 
11
More examples can be found in the supplementary material.
 
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Metadaten
Titel
Multi-view Outlier Detection
verfasst von
Zhengming Ding
Handong Zhao
Yun Fu
Copyright-Jahr
2019
Buch
Learning Representation for Multi-View Data Analysis

Print ISBN: 978-3-030-00733-1

Electronic ISBN: 978-3-030-00734-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-00734-8_4