Robust Joint and Individual Variance Explained

@article{Sagonas2017RobustJA,
  title={Robust Joint and Individual Variance Explained},
  author={Christos Sagonas and Yannis Panagakis and Alina Leidinger and Stefanos P. Zafeiriou},
  journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2017},
  pages={5739-5748}
}
Discovering the common (joint) and individual subspaces is crucial for analysis of multiple data sets, including multi-view and multi-modal data. Several statistical machine learning methods have been developed for discovering the common features across multiple data sets. The most well studied family of the methods is that of Canonical Correlation Analysis (CCA) and its variants. Even though the CCA is a powerful tool, it has several drawbacks that render its application challenging for… CONTINUE READING

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