title: Probabilistic self-organizing maps for qualitative data.
abstract: We present a self-organizing map model to study qualitative data (also called categorical data). It is based on a probabilistic framework which does not assume any prespecified distribution of the input data. Stochastic approximation theory is used to develop a learning rule that builds an approximation of a discrete distribution on each unit. This way, the internal structure of the input dataset and the correlations between components are revealed without the need of a distance measure among the input values. Experimental results show the capabilities of the model in visualization and unsupervised learning tasks.
authors.affiliation: Department of Computer Languages and Computer Science, University of Málaga, Bulevar Louis Pasteur 35, Málaga, Spain. ezeqlr@lcc.uma.es
authors.forename: Ezequiel
authors.initials: E
authors.lastname: López-Rubio
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007858:Learning; D008959:Models, Neurological; D015233:Models, Statistical; D011336:Probability