Sparse Decomposition Methods for Spatio-Temporal Anomaly Detection

Hao Yan (), Ziyue Li (), Xinyu Zhao () and Jiuyun Hu ()
Additional contact information
Hao Yan: Arizona State University
Ziyue Li: University of Cologne
Xinyu Zhao: Arizona State University
Jiuyun Hu: Arizona State University

A chapter in Multimodal and Tensor Data Analytics for Industrial Systems Improvement, 2024, pp 185-206 from Springer

Abstract: 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.

Keywords: Anomaly and hotspot detection; Spatial and temporal data; Deep sparse decomposition method (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-031-53092-0_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031530920

DOI: 10.1007/978-3-031-53092-0_9

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().