@inproceedings{6716,
  title = {Gaussian Mixture Modeling with Gaussian Process Latent Variable Models},
  journal = {Pattern Recognition: 32nd DAGM Symposium},
  booktitle = {Pattern Recognition},
  abstract = {Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.},
  pages = {271-282},
  editors = {Goesele, M. , S. Roth, A. Kuijper, B. Schiele, K. Schindler},
  publisher = {Springer},
  organization = {Max-Planck-Gesellschaft},
  institution = {Deutsche Arbeitsgemeinschaft für Mustererkennung},
  school = {Biologische Kybernetik},
  address = {Berlin, Germany},
  month = sep,
  year = {2010},
  slug = {6716},
  author = {Nickisch, H. and Rasmussen, CE.},
  month_numeric = {9}
}