One-Class Classification based on Extreme Learning and Geometric Class Information

Alexandros Iosifidis, Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas

Research output: Contribution to journalArticle (Academic Journal)peer-review

16 Citations (Scopus)
302 Downloads (Pure)

Abstract

In this paper, we propose an Extreme Learning Machine-based one-class classification method that exploits geometric class information. We formulate the proposed method to exploit data representations in the feature space determined by the network hidden layer outputs, as well as in ELM spaces of arbitrary dimensions. We show that the exploitation of geometric class information enhances performance. We evaluate the proposed approach in publicly available datasets and compare its performance with the recently proposed One-Class Extreme Learning Machine algorithm, as well as with standard and recently proposed one-class classifiers. Experimental results show that the proposed method consistently outperforms the remaining approaches.
Original languageEnglish
Pages (from-to)577-592
Number of pages16
JournalNeural Processing Letters
Volume45
Issue number2
Early online date1 Aug 2016
DOIs
Publication statusPublished - Apr 2017

Keywords

  • One-class classification
  • Novelty detection
  • Big Data
  • Extreme Learning Machine

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