We introduce a compositional generative model for topographic mapping of tree-structured data. It exploits a scalable bottom-up hidden tree Markov model to achieve a recursive topographic mapping of hierarchical information. The model allows for an efficient exploitation of contextual information from shared substructures by recursive upward propagation on the tree structure and by allowing it to distribute across the map. Experimental results show that the model yields to a topographically ordered mapping of the substructures in the input data.

Compositional generative mapping of structured data

SPERDUTI, ALESSANDRO
2010

Abstract

We introduce a compositional generative model for topographic mapping of tree-structured data. It exploits a scalable bottom-up hidden tree Markov model to achieve a recursive topographic mapping of hierarchical information. The model allows for an efficient exploitation of contextual information from shared substructures by recursive upward propagation on the tree structure and by allowing it to distribute across the map. Experimental results show that the model yields to a topographically ordered mapping of the substructures in the input data.
2010
Proceedings of the 2010 IEEE International Joint Conference on Neural Networks
9781424469178
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/2420578
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