A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples

@article{Zhang2011ANE,
  title={A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples},
  author={Jianchun Zhang and Daoqiang Zhang},
  journal={Pattern Recognition},
  year={2011},
  volume={44},
  pages={1162-1171}
}
Correlated information between multiple views can provide useful information for building robust classifiers. One way to extract correlated features from different views is using canonical correlation analysis (CCA). However, CCA is an unsupervised method and can not preserve discriminant information in feature extraction. In this paper, we first incorporate discriminant information into CCA by using random cross-view correlations between within-class examples. Because of the random property… CONTINUE READING
17 Citations
24 References
Similar Papers

References

Publications referenced by this paper.
Showing 1-10 of 24 references

Multiview fisher discriminant analysis

  • T. Diethe, D. R. Hardoon, J. Shawe-Taylor
  • Technical report, at the NIPS workshop Learning…
  • 2008
1 Excerpt

Similar Papers

Loading similar papers…