Semi-Supervised Dimension Reduction Using Trace Ratio Criterion

  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},
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
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