Exploiting local and global geometric data relationships in Support Vector Data Description

Vasileios Mygdalis; Anastasios Tefas; Ioannis Pitas

doi:10.1109/ICPR.2016.7899685

Exploiting local and global geometric data relationships in Support Vector Data Description

Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas

Department of Electrical & Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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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.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Subtitle of host publication	Proceedings of a meeting held 4-8 December 2016, Cancun, Mexico
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	515-519
Number of pages	5
ISBN (Electronic)	9781509048472
ISBN (Print)	9781509048489
DOIs	https://doi.org/10.1109/ICPR.2016.7899685
Publication status	Published - May 2017
Event	International Conference on Pattern Recognition - Cancun, Mexico Duration: 4 Dec 2016 → 8 Dec 2016

Conference

Conference	International Conference on Pattern Recognition
Abbreviated title	ICPR2016
Country/Territory	Mexico
City	Cancun
Period	4/12/16 → 8/12/16

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Mygdalis, V., Tefas, A., & Pitas, I. (2017). Exploiting local and global geometric data relationships in Support Vector Data Description. In 2016 23rd International Conference on Pattern Recognition (ICPR 2016): Proceedings of a meeting held 4-8 December 2016, Cancun, Mexico (pp. 515-519). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICPR.2016.7899685

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Mygdalis, V, Tefas, A & Pitas, I 2017, Exploiting local and global geometric data relationships in Support Vector Data Description. in 2016 23rd International Conference on Pattern Recognition (ICPR 2016): Proceedings of a meeting held 4-8 December 2016, Cancun, Mexico. Institute of Electrical and Electronics Engineers (IEEE), pp. 515-519, International Conference on Pattern Recognition, Cancun, Mexico, 4/12/16. https://doi.org/10.1109/ICPR.2016.7899685

Exploiting local and global geometric data relationships in Support Vector Data Description. / Mygdalis, Vasileios; Tefas, Anastasios; Pitas, Ioannis.

2016 23rd International Conference on Pattern Recognition (ICPR 2016): Proceedings of a meeting held 4-8 December 2016, Cancun, Mexico. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 515-519.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AB - 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.

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Mygdalis V, Tefas A, Pitas I. Exploiting local and global geometric data relationships in Support Vector Data Description. In 2016 23rd International Conference on Pattern Recognition (ICPR 2016): Proceedings of a meeting held 4-8 December 2016, Cancun, Mexico. Institute of Electrical and Electronics Engineers (IEEE). 2017. p. 515-519 Epub 2017 Apr 24. doi: 10.1109/ICPR.2016.7899685

Exploiting local and global geometric data relationships in Support Vector Data Description

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