Visualization of Structured Data via Generative Probabilistic Modeling
Similarity-Based Clustering
A weighted voting summarization of SOM ensembles
Data Mining and Knowledge Discovery
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Neurocomputing
An input-output hidden Markov model for tree transductions
Neurocomputing
| Hi-index | 0.00 |
In this paper, we present a probabilistic generative approach for constructing topographic maps of tree-structured data. Our model defines a low-dimensional manifold of local noise models, namely, (hidden) Markov tree models, induced by a smooth mapping from low-dimensional latent space. We contrast our approach with that of topographic map formation using recursive neural-based techniques, namely, the self-organizing map for structured data (SOMSD) (Hagenbuchner et al., 2003). The probabilistic nature of our model brings a number of benefits: (1) naturally defined cost function that drives the model optimization; (2) principled model comparison and testing for overfitting; (3) a potential for transparent interpretation of the map by inspecting the underlying local noise models; (4) natural accommodation of alternative local noise models implicitly expressing different notions of structured data similarity. Furthermore, in contrast with the recursive neural-based approaches, the smooth nature of the mapping from the latent space to the local model space allows for calculation of magnification factors-a useful tool for the detection of data clusters. We demonstrate our approach on three data sets: a toy data set, an artificially generated data set, and on a data set of images represented as quadtrees.