Bottom-Up Generative Modeling of Tree-Structured Data.

We introduce a compositional probabilistic model for treestructured data that defines a bottom-up generative process from the leaves to the root of a tree. Contextual state transitions are introduced from the joint configuration of the children to the parent nodes, allowing hidden states to model the co-occurrence of substructures among the child subtrees. A mixed memory approximation is proposed to factorize the joint transition matrix as a mixture of pairwise transitions. A comparative experimental analysis shows that the proposed approach is able to better model deep structures with respect to top-down approaches.