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Sparse Mixture Conditional Density Estimation by Superficial Regularization

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

Abstract:
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
urn:nbn:de:swb:90-351297
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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