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Sparse Mixture Conditional Density Estimation by Superficial Regularization

Krauthausen, Peter; Ruoff, Patrick; Hanebeck, Uwe D.

In this paper, the estimation of conditional densities between continuous random variables from noisy samples is considered. The conditional densities are modeled as heteroscedastic Gaussian mixture densities allowing for closed-form solution of Bayesian inference with full-densities. The main contributions of this paper are an improved generalization quality of the estimates by the introduction of a superficial regularizer, the consideration of model uncertainty relative to local data densities by means of adaptive covariances, and the proposition of an efficient distance-based estimation algorithm. This algorithm corresponds to an iterative nested optimization scheme, optimizing hyper-parameters, component placement, and mixture weights. The obtained solutions are sparse, smooth, and generalize well as benchmark experiments, e.g., in nonlinear filtering show.

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Volltext §
DOI: 10.5445/IR/1000035129
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-1-4577-0267-9
KITopen-ID: 1000035129
Erschienen in Proceedings of the 14th International Conference on Information Fusion (Fusion 2011), Chicago, Illinois, USA, 5-8 July 2011
Verlag IEEE, Piscataway
Seiten 8 S.
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