Out-of-Sample Generalizations for Supervised Manifold Learning for Classification

@article{Vural2016OutofSampleGF,
  title={Out-of-Sample Generalizations for Supervised Manifold Learning for Classification},
  author={Elif Vural and Christine Guillemot},
  journal={IEEE Transactions on Image Processing},
  year={2016},
  volume={25},
  pages={1410-1424}
}
Supervised manifold learning methods for data classification map high-dimensional data samples to a lower dimensional domain in a structure-preserving way while increasing the separation between different classes. Most manifold learning methods compute the embedding only of the initially available data; however, the generalization of the embedding to novel… CONTINUE READING