Online Data Thinning via Multi-Subspace Tracking

@article{Hunt2019OnlineDT,
  title={Online Data Thinning via Multi-Subspace Tracking},
  author={Xin Jiang Hunt and Rebecca M. Willett},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2019},
  volume={41},
  pages={1173-1187}
}
  • X. Hunt, R. Willett
  • Published 2019
  • Mathematics, Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine Intelligence
In an era of ubiquitous large-scale streaming data, the availability of data far exceeds the capacity of expert human analysts. In many settings, such data is either discarded or stored unprocessed in data centers. This paper proposes a method of online data thinning, in which large-scale streaming datasets are winnowed to preserve unique, anomalous, or salient elements for timely expert analysis. At the heart of this proposed approach is an online anomaly detection method based on dynamic, low… Expand
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