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Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection

In: Multimodal and Tensor Data Analytics for Industrial Systems Improvement

Author

Listed:
  • Hao Yan

    (Arizona State University)

  • Ziyue Li

    (University of Cologne)

  • Xinyu Zhao

    (Arizona State University)

  • Jiuyun Hu

    (Arizona State University)

Abstract

Anomaly detection constitutes a critical field of research, concerned with the identification of rare, atypical, or unexpected patterns within a dataset. Within the existing literature, the majority of anomaly detection techniques lack the capability to localize the anomalies. Recently, techniques such as sparse anomaly decomposition methods possess the distinctive ability to not only detect but also pinpoint the location of the anomalies concurrently. In the subsequent sections of this chapter, an exhaustive review of existing anomaly decomposition techniques will be conducted, with a particular emphasis on the smooth sparse decomposition method. Following this, several contemporary extensions to sparse decomposition methods will be explored, resulting in a discussion on the prospective directions for future research in this domain.

Suggested Citation

  • Hao Yan & Ziyue Li & Xinyu Zhao & Jiuyun Hu, 2024. "Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection," Springer Optimization and Its Applications, in: Nathan Gaw & Panos M. Pardalos & Mostafa Reisi Gahrooei (ed.), Multimodal and Tensor Data Analytics for Industrial Systems Improvement, pages 185-206, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-53092-0_9
    DOI: 10.1007/978-3-031-53092-0_9
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