Maximum margin/volume outlier detection

Li, S; Tsang, IW

Maximum margin/volume outlier detection

Li, S Tsang, IW

Permalink

Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2011, pp. 385 - 392
Issue Date:: 2011-12-01

Closed Access

	Filename	Description	Size
	06103353.pdf	Published version	240.1 kB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Li, S	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI, 2011, pp. 385 - 392	en_US
dc.identifier.isbn	9780769545967	en_US
dc.identifier.issn	1082-3409	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120464
dc.description.abstract	Due to the absence or scarcity of outliers, designing a robust outlier detector is very challenging. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers from a given data set, which demonstrates superior performance in outlier detection. However, the resultant learning task is formulated as a Mixed Integer Programming (MIP) problem, which is computationally hard. To this end, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection. Specifically, we propose an effective procedure to find a largely violated labeling vector for identifying rare outliers from abundant normal patterns, and its convergence is also presented. Then, a set of largely violated labeling vectors are combined by multiple kernel learning methods to robustly detect outliers. Besides these, to further enhance the efficacy of our outlier detector, we also explore the use of maximum volume criterion to measure the quality of separation between outliers and normal patterns. This criterion can be easily incorporated into our proposed framework by introducing an additional regularization. Comprehensive experiments on real-world data sets verify that the outlier detectors using the two proposed criteria outperform existing outlier detection methods. © 2011 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI	en_US
dc.relation.isbasedon	10.1109/ICTAI.2011.63	en_US
dc.title	Maximum margin/volume outlier detection	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Due to the absence or scarcity of outliers, designing a robust outlier detector is very challenging. In this paper, we first propose to use the maximum margin criterion to sift unknown outliers from a given data set, which demonstrates superior performance in outlier detection. However, the resultant learning task is formulated as a Mixed Integer Programming (MIP) problem, which is computationally hard. To this end, we alter the recently developed label generating technique, which efficiently solves a convex relaxation of the MIP problem of outlier detection. Specifically, we propose an effective procedure to find a largely violated labeling vector for identifying rare outliers from abundant normal patterns, and its convergence is also presented. Then, a set of largely violated labeling vectors are combined by multiple kernel learning methods to robustly detect outliers. Besides these, to further enhance the efficacy of our outlier detector, we also explore the use of maximum volume criterion to measure the quality of separation between outliers and normal patterns. This criterion can be easily incorporated into our proposed framework by introducing an additional regularization. Comprehensive experiments on real-world data sets verify that the outlier detectors using the two proposed criteria outperform existing outlier detection methods. © 2011 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/120464