@techreport{nickisch10dgplvm,
   author = {Nickisch, H.  and C. E. Rasmussen},
   title = {Gaussian Mixture Modeling with Gaussian Process Latent Variable Models},
   year = {2010},
   month = {06},
   institution like {%Cornell University Library%},
   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.},
   URL = {http://arxiv.org/abs/1006.3640}
}