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Neural Processing Letters

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01-08-2016

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

Authors: Alexandros Iosifidis, Vasileios Mygdalis, Anastasios Tefas, Ioannis Pitas

Published in: Neural Processing Letters | Issue 2/2017

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Abstract

In this paper, we propose an extreme learning machine (ELM)-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.

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Title: One-Class Classification Based on Extreme Learning and Geometric Class Information
Authors: Alexandros Iosifidis
Vasileios Mygdalis
Anastasios Tefas
Ioannis Pitas
Publication date: 01-08-2016
Publisher: Springer US
Published in: Neural Processing Letters / Issue 2/2017
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-016-9541-y

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