Probabilistic Self-Organizing Maps for multivariate sequences

This paper describes a new algorithm to learn a new probabilistic Self-Organizing Map for not independent and not identically distributed data set. This new paradigm probabilistic self-organizing map uses HMM (Hidden Markov Models) formalism and introduces relationships between the states of the map. The map structure is integrated in the parameter estimation of Markov model using a neighborhood function to learn a topographic clustering. We have applied this novel model to cluster and to reconstruct the data captured using a WACOM tablet.