Publication
Title
Exploiting local and global geometric data relationships in Support Vector Data Description
Author
Abstract
In this paper, we describe a one-class classification method based on Support Vector Data Description, which exploits multiple graph structures in its optimization process. We derive in a generic solution which can be employed for supervised one-class classification tasks. The devised method can produce linear or non-linear decision functions, depending on the adopted kernel function. In our experiments, we simultaneously adopted two graphs that describe local and global geometric training data relationships, respectively. We evaluated the proposed classifier in publicly available datasets, where its performance compared favorably against closely related methods.
Language
English
Source (journal)
Proceedings of the IAPR international conference on pattern recognition / IAPR International Conference on Pattern Recognition. - Los Alamitos
Source (book)
2016 23rd International Conference on Pattern Recognition (ICPR), 04-08 December, 2016, Cancun, Mexico
Publication
Los alamitos : IEEE Computer Society Press , 2016
ISSN
1051-4651
ISBN
978-1-5090-4847-2
DOI
10.1109/ICPR.2016.7899685
Volume/pages
(2016) , p. 515-519
ISI
000406771300088
Full text (Publisher's DOI)
UAntwerpen
Publication type
Subject
External links
Web of Science
Record
Identifier
Creation 17.10.2023
Last edited 17.06.2024
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