Uncertainty-Aware Principal Component Analysis

@article{Grtler2020UncertaintyAwarePC,
  title={Uncertainty-Aware Principal Component Analysis},
  author={Jochen G{\"o}rtler and Thilo Spinner and Dirk Streeb and D. Weiskopf and O. Deussen},
  journal={IEEE Transactions on Visualization and Computer Graphics},
  year={2020},
  volume={26},
  pages={822-831}
}
  • Jochen Görtler, Thilo Spinner, +2 authors O. Deussen
  • Published 2020
  • Computer Science, Mathematics, Medicine
  • IEEE Transactions on Visualization and Computer Graphics
  • We present a technique to perform dimensionality reduction on data that is subject to uncertainty. [...] Key Method We derive a representation of the PCA sample covariance matrix that respects potential uncertainty in each of the inputs, building the mathematical foundation of our new method: uncertainty-aware PCA. In addition to the accuracy and performance gained by our approach over sampling-based strategies, our formulation allows us to perform sensitivity analysis with regard to the uncertainty in the data…Expand Abstract
    3 Citations

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