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

Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas

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

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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 languageEnglish
Title of host publication2016 23rd International Conference on Pattern Recognition (ICPR 2016)
Subtitle of host publicationProceedings of a meeting held 4-8 December 2016, Cancun, Mexico
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)9781509048472
ISBN (Print)9781509048489
Publication statusPublished - May 2017
EventInternational Conference on Pattern Recognition - Cancun, Mexico
Duration: 4 Dec 20168 Dec 2016


ConferenceInternational Conference on Pattern Recognition
Abbreviated titleICPR2016


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