Publication Details

SELECT * FROM publications WHERE Record_Number=11016
Reference TypeConference Paper
Author(s)Gebhardt, G. H. W.; Kupcsik, A.; Neumann, G.
TitleLearning Subspace Conditional Embedding Operators
Journal/Conference/Book TitleLarge-Scale Kernel Learning Workshop at ICML 2015
AbstractEstimating and predicting partially observable states of a high-dimensional and highly stochastic system is still a challenging problem in machine learning and robotics. Recently, kernel methods for nonparametric inference (Song et al., 2013) have been introduced which allow belief propagation with arbitrary probability distributions. However, one of the main limiting factors is that the provided algorithms scale cubically with the number of samples in the kernel matrices. In this paper, we present an extension to these nonparametric methods for inference that uses only a subset of the samples for the state representation, while still using the full data set for learning the conditional operators. Our approach is able to significantly reduce the learning and run time of the algorithm, while maintaing or even improving the performance.
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