A Compressed PCA Subspace Method for Anomaly Detection in High-Dimensional Data

@article{Ding2013ACP,
  title={A Compressed PCA Subspace Method for Anomaly Detection in High-Dimensional Data},
  author={Qi Ding and Eric D. Kolaczyk},
  journal={IEEE Transactions on Information Theory},
  year={2013},
  volume={59},
  pages={7419-7433}
}
Random projection is widely used as a method of dimension reduction. In recent years, its combination with standard techniques of regression and classification has been explored. Here, we examine its use for anomaly detection in high-dimensional settings, in conjunction with principal component analysis (PCA) and corresponding subspace detection methods. We assume a so-called spiked covariance model for the underlying data generation process and a Gaussian random projection. We adopt a… CONTINUE READING
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