Canonical Correlation Analysis on Data With Censoring and Error Information

@article{Sun2013CanonicalCA,
  title={Canonical Correlation Analysis on Data With Censoring and Error Information},
  author={Jianyong Sun and Simeon Keates},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2013},
  volume={24},
  pages={1909-1919}
}
We developed a probabilistic model for canonical correlation analysis in the case when the associated datasets are incomplete. This case can arise where data entries either contain measurement errors or are censored (i.e., nonignorable missing) due to uncertainties in instrument calibration and physical limitations of devices and experimental conditions. The aim of our model is to estimate the true correlation coefficients, through eliminating the effects of measurement errors and abstracting… CONTINUE READING
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