论文信息 - Visualisation of tree-structured data through generative probabilistic modelling

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.

Peter Tiño | Nikolaos Gianniotis

[1] Peter Tiño,et al. A generative probabilistic approach to visualizing sets of symbolic sequences , 2004, KDD '04.

[2] Barbara Hammer,et al. Merge SOM for temporal data , 2005, Neurocomputing.

[3] T. Heskes. Energy functions for self-organizing maps , 1999 .

[4] Aluizio F. R. Araújo,et al. A Taxonomy for Spatiotemporal Connectionist Networks Revisited: The Unsupervised Case , 2003, Neural Computation.

[5] Alessio Micheli,et al. A general framework for unsupervised processing of structured data , 2004, Neurocomputing.

[6] Teuvo Kohonen,et al. The self-organizing map , 1990 .

[7] Lawrence R. Rabiner,et al. A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[8] Ah Chung Tsoi,et al. A self-organizing map for adaptive processing of structured data , 2003, IEEE Trans. Neural Networks.

[9] Christopher M. Bishop,et al. GTM: The Generative Topographic Mapping , 1998, Neural Computation.

[10] Paulo Gonçalves,et al. Computational methods for hidden Markov tree models-an application to wavelet trees , 2004, IEEE Transactions on Signal Processing.