Visualisation of tree-structured data through generative probabilistic modelling

We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A natural measure of likelihood arises as a cost function that guides the model fitting. We compare our approach with an existing neural-based methodology for constructing topographic maps of directed acyclic graphs. We argue that the probabilistic nature of our model brings several advantages, such as principled interpretation of the visualisation plots.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Nikolaos Gianniotis, Peter Tino
• Keywords: ESANN 2007, Generative Probabilistic Model, Model, Neural Networks, Tree Structured Data