@inproceedings{6639,
  title = {Weakly-Paired Maximum Covariance Analysis for Multimodal Dimensionality Reduction and Transfer Learning},
  journal = {Computer vision - ECCV 2010: 11th European Conference on Computer Vision},
  booktitle = {Computer Vision – ECCV 2010},
  abstract = {We study the problem of multimodal dimensionality reduction assuming that data samples can be missing at training time,
  and not all data modalities may be present at application time. Maximum covariance analysis, as a generalization of PCA, has
  many desirable properties, but its application to practical problems is limited by its need for perfectly paired data. We
  overcome this limitation by a latent variable approach that allows working with weakly paired data and is still able to
  efficiently process large datasets using standard numerical routines. The resulting weakly paired maximum covariance analysis
  often finds better representations than alternative methods, as we show in two exemplary tasks: texture discrimination and transfer learning.},
  pages = {566-579},
  editors = {Daniilidis, K. , P. Maragos, N. Paragios},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2010},
  slug = {6639},
  author = {Lampert, CH. and Kroemer, O.},
  month_numeric = {9}
}