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Neural Computing and Applications
Published in: Neural Computing and Applications 8/2019

21-11-2017 | Original Article

LGND: a new method for multi-class novelty detection

Published in: Neural Computing and Applications | Issue 8/2019

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Abstract

Multi-class novelty detection is a crucial yet challenging aspect for recognition systems. Several methods have been presented, which either concatenate multiple classes into a large artificial super-class, or combine several independent classifiers of each known class, or utilize the results of multi-class classifiers. However, these methods ignore the correlation within each class, or cannot be elegantly formulated in a joint model. To overcome these limitations, we propose a new local and global novelty detection model (LGND). Different from the previous approaches, LGND incorporates the local correlation with the global regularization in a unified framework. This new optimization model boils down to a convex quadratic programming with guaranteed global optimum solution. Furthermore, comprehensive discussions, including the relationship between locality and globality, the discussion on the parameters in LGND and the connections to multi-class classification, are also presented. LGND opens up a new way for multi-class novelty detection from both local and global perspectives. Experimental results on Corel5k, Caltech-256, and five UCI data sets confirm that LGND outperforms or is at least comparable to state-of-the-art methods.
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Metadata
Title
LGND: a new method for multi-class novelty detection
Publication date
21-11-2017
Published in
Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3270-7

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