@inproceedings{4929,
  title = {Colored Maximum Variance Unfolding},
  journal = {Advances in Neural Information Processing Systems 20: 21st Annual Conference on Neural Information Processing Systems 2007},
  booktitle = {Advances in neural information processing systems 20},
  abstract = {Maximum variance unfolding (MVU) is an effective heuristic for dimensionality reduction. It produces a low-dimensional representation of the data by maximizing the variance of their embeddings while preserving the local distances of the
  original data. We show that MVU also optimizes a statistical dependence measure which aims to retain the identity of individual observations under the distancepreserving constraints. This general view allows us to design "colored" variants of MVU, which produce low-dimensional representations for a given task, e.g. subject to class labels or other side information.},
  pages = {1385-1392},
  editors = {Platt, J. C., D. Koller, Y. Singer, S. Roweis},
  publisher = {Curran},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Red Hook, NY, USA},
  month = sep,
  year = {2008},
  slug = {4929},
  author = {Song, L. and Smola, AJ. and Borgwardt, K. and Gretton, A.},
  month_numeric = {9}
}