On the Product Probability Kernel in Sparse Gaussian Process Models  Bjørn Sand Jensen, Jens Brehm Nielsen, Jan Larsen
 Abstract  In this report we consider the particular use of the product probability kernel in models based on the pseudoinput formulation of Gaussian process priors. In particular we focus on modeling inputs as generative mixture models which in effect “forces” the pseudoinputs to be densities as well. We focus on the learning and interpretation of these pseudoinput/densities in a clustering sense, and provide a few connections with other formulations of covariance structure for pseudo inputs.  Keywords  Product probability kernel, Kernel Methods, Gaussian Processes, Clustering, Generative Kernels  Type  Misc [Other]  Year  2011 Month October  Publisher  DTU  IMM  BibTeX data  [bibtex]  IMM Group(s)  Intelligent Signal Processing 
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