Semi-Supervised Dimension Reduction Using Trace Ratio Criterion

@article{Huang2012SemiSupervisedDR,
  title={Semi-Supervised Dimension Reduction Using Trace Ratio Criterion},
  author={Yi Huang and Dong Xu and Feiping Nie},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  year={2012},
  volume={23},
  pages={519-526}
}
In this brief, we address the trace ratio (TR) problem for semi-supervised dimension reduction. We first reformulate the objective function of the recent work semi-supervised discriminant analysis (SDA) in a TR form. We also observe that in SDA the low-dimensional data representation F is constrained to be in the linear subspace spanned by the training data matrix X (i.e., F = XT W). In order to relax this hard constraint, we introduce a flexible regularizer ||F - XT W||2 which models the… CONTINUE READING
Highly Cited
This paper has 174 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 39 extracted citations

174 Citations

0204060'13'15'17
Citations per Year
Semantic Scholar estimates that this publication has 174 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 26 references

Similar Papers

Loading similar papers…