Compositional generative mapping of structured data

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.