@inproceedings{2281,
  title = {Learning to Find Pre-Images},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 16},
  abstract = {We consider the problem of reconstructing patterns from a feature map.  Learning algorithms using kernels to operate in a reproducing kernel Hilbert space (RKHS) express their solutions in terms of input points mapped into the RKHS. We
  introduce a technique based on kernel principal component analysis and regression to reconstruct corresponding patterns in the input space (aka pre-images) and review its performance in several applications requiring the construction of pre-images. The introduced technique avoids
  difficult and/or unstable numerical optimization, is easy to
  implement and, unlike previous methods, permits the computation of pre-images in discrete input spaces.},
  pages = {449-456},
  editors = {S Thrun and LK Saul and B Sch{\"o}lkopf},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jun,
  year = {2004},
  author = {Bakir, GH. and Weston, J. and Sch{\"o}lkopf, B.},
  month_numeric = {6}
}