Recursive Self-organizing Map as a Contractive Iterative Function System

Recently, there has been a considerable research activity in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, the representational capabilities and internal representations of the models are not well understood. We rigorously analyze a generalization of the Self-Organizing Map (SOM) for processing sequential data, Recursive SOM (RecSOM [1]), as a non-autonomous dynamical system consisting of a set of fixed input maps. We show that contractive fixed input maps are likely to produce Markovian organizations of receptive fields on the RecSOM map. We derive bounds on parameter β (weighting the importance of importing past information when processing sequences) under which contractiveness of the fixed input maps is guaranteed.

[1]  John G. Taylor,et al.  The temporal Kohönen map , 1993, Neural Networks.

[2]  Alessio Micheli,et al.  Recursive self-organizing network models , 2004, Neural Networks.

[3]  KEIICHI HORIO,et al.  Feedback Self-Organizing Map and its Application to Spatio-Temporal Pattern Classification , 2001, Int. J. Comput. Intell. Appl..

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

[5]  James A. Reggia,et al.  Temporally Asymmetric Learning Supports Sequence Processing in Multi-Winner Self-Organizing Maps , 2004, Neural Computation.

[6]  Peter Tiňo,et al.  Topographic Organization of Receptive Fields in Recursive Self-Organizing Map , .

[7]  Jukka Heikkonen,et al.  Recurrent SOM with local linear models in time series prediction , 1998, ESANN.

[8]  T. Kohonen Self-organized formation of topographically correct feature maps , 1982 .

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

[10]  Thomas Voegtlin,et al.  Recursive self-organizing maps , 2002, Neural Networks.

[11]  T. Kohonen Self-Organized Formation of Correct Feature Maps , 1982 .

[12]  Marc Strickert,et al.  Neural Gas for Sequences , 2003 .

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

[14]  Peter Tiño,et al.  On Non-markovian Topographic Organization of Receptive Fields in Recursive Self-organizing Map , 2005, ICNC.

[15]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.