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Low-Rank Outlier Detection

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Low-Rank and Sparse Modeling for Visual Analysis

Abstract

In this chapter, we present a novel low-rank outlier detection approach, which incorporates a low-rank constraint into the support vector data description (SVDD) model. Different from the traditional SVDD, our approach learns multiple hyper-spheres to fit the normal data. The low-rank constraint helps us group the complicated dataset into several clusters dynamically. We present both primal and dual solutions to solve this problem, and provide the detailed strategy of outlier detection. Moreover, the kernel-trick used in SVDD becomes unnecessary in our approach, which implies that the training time and memory space could be substantially reduced. The performance of our approach, along with other related methods, was evaluated using three image databases. Results show our approach outperforms other methods in most scenarios.

\(\copyright \) [2014] IEEE. This chapter is reprinted with permission from IEEE. “Locality Linear Fitting One-class SVM with Low-Rank Constraints for Outlier Detection”, International Joint Conference on Neural Networks (IJCNN), 2014.

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Acknowledgments

This research is supported in part by the NSF CNS award 1314484, Office of Naval Research award N00014-12-1-1028, Air Force Office of Scientific Research award FA9550-12-1-0201, and U.S. Army Research Office grant W911NF-13-1-0160.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Sheng Li & Ming Shao

  2. Department of Electrical and Computer Engineering and College of Computer and Information Science, Northeastern University, 360 Huntington Avenue, Boston, MA, 02115, USA

    Yun Fu

Authors
  1. Sheng Li
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  2. Ming Shao
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  3. Yun Fu
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Correspondence to Sheng Li .

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Editors and Affiliations

  1. Northeastern University, Boston, Massachusetts, USA

    Yun Fu

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© 2014 Springer International Publishing Switzerland

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Li, S., Shao, M., Fu, Y. (2014). Low-Rank Outlier Detection. In: Fu, Y. (eds) Low-Rank and Sparse Modeling for Visual Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-12000-3_9

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  • DOI: https://doi.org/10.1007/978-3-319-12000-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11999-1

  • Online ISBN: 978-3-319-12000-3

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