A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data

@article{Chen2012AUD,
  title={A unified dimensionality reduction framework for semi-paired and semi-supervised multi-view data},
  author={Xiaohong Chen and Songcan Chen and Hui Xue and Xudong Zhou},
  journal={Pattern Recognition},
  year={2012},
  volume={45},
  pages={2005-2018}
}
Canonical correlation analysis (CCA) is a popular and powerful dimensionality reduction method to analyze paired multi-view data. However, when facing semi-paired and semi-supervised multi-view data which widely exist in real-world problems, CCA usually performs poorly due to its requirement of data pairing between different views and un-supervision in nature. Recently, several extensions of CCA have been proposed, however, they just handle the semi-paired scenario by utilizing structure… CONTINUE READING
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