Supervised relational learning over labeled graphs, e.g. via recursive neural nets, received considerable attention from the connectionist community. Surprisingly, with the exception of recursive self organizing maps, unsupervised paradigms have been far less investigated. In particular, no algorithms for density estimation over graphs are found in the literature. This paper introduces first a formal notion of probability density function (pdf) over graphical spaces. It then proposes a maximum-likelihood pdf estimation technique, relying on the joint optimization of a recursive encoding network and a constrained radial basis functions-like net. Preliminary experiments on synthetically generated samples of labeled graphs are analyzed and tested statistically.

Trentin, E., & L., R. (2009). A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains. In Proceedings of ICANN2009 (International Conference on Artificial Neural Networks) (pp.40-49). Berlin : Springer [10.1007/978-3-642-04274-4_5].

A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains

TRENTIN, EDMONDO;
2009

Abstract

Supervised relational learning over labeled graphs, e.g. via recursive neural nets, received considerable attention from the connectionist community. Surprisingly, with the exception of recursive self organizing maps, unsupervised paradigms have been far less investigated. In particular, no algorithms for density estimation over graphs are found in the literature. This paper introduces first a formal notion of probability density function (pdf) over graphical spaces. It then proposes a maximum-likelihood pdf estimation technique, relying on the joint optimization of a recursive encoding network and a constrained radial basis functions-like net. Preliminary experiments on synthetically generated samples of labeled graphs are analyzed and tested statistically.
978-3-642-04273-7
Trentin, E., & L., R. (2009). A Maximum-Likelihood Connectionist Model for Unsupervised Learning over Graphical Domains. In Proceedings of ICANN2009 (International Conference on Artificial Neural Networks) (pp.40-49). Berlin : Springer [10.1007/978-3-642-04274-4_5].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/24212
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