Application of a local outlier detection algorithm based on high-dimensional subspaces in near-infrared spectroscopy
This paper introduces a new method for determining local subspaces, which combines local sparsity with adaptive neighborhood selection. Then we use the jump degree to adaptively determine the anomaly threshold, achieving the recognition of outliers.
Authors
- Weiyao Hu (ORCID: https://orcid.org/0000-0002-1373-4447)
- Hao Zhang (ORCID: https://orcid.org/0000-0002-6227-9815)
- Jinfeng Zhang (ORCID: https://orcid.org/0000-0002-5954-9052)
- Xiaoli Bai
- Yuhua Qin
Institutions
- Qingdao University of Science and Technology (CN)
- China Tobacco (CN)
Publication Details
- Journal
- Journal of Analytical Atomic Spectrometry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d4ja00366g
- Citations
- 1
- Primary Topic
- Fault Detection and Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 1.21