# 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 Daniel Weiskopf and Oliver Deussen}, journal={IEEE Transactions on Visualization and Computer Graphics}, year={2020}, volume={26}, pages={822-831} }

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

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