Conference paper

Publication year: 2005

Pages: 69-97

Journal: Neurocomputing

Volume number: 63

ISSN: 0925-2312

eISSN: 1872-8286

DOI Link: https://doi.org/10.1016/j.neucom.2004.01.190

Conference: 11th European Symposium on Artificial Neural Networks (ESANN)

Publisher: Elsevier

Abstract: 
The self-organizing map (SOM) is a valuable tool for data visualization and data mining for potentially high-dimensional data of an a priori fixed dimensionality. We investigate SOMs for sequences and propose the SOM-S architecture for sequential data. Sequences of potentially infinite length are recursively processed by integrating the currently presented item and the recent map activation, as proposed in the SOMSD presented in (IEEE Trans. Neural Networks 14(3) (2003) 491). We combine that approach with the hyperbolic neighborhood of Ritter (Proceedings of PKDD-01, Springer, Berlin, 2001 pp. 338-349), in order to account for the representation of possibly exponentially increasing sequence diversification over time. Discrete and real-valued sequences can be processed efficiently with this method, as we will show in experiments. Temporal dependencies can be reliably extracted from a trained SOM. U-matrix methods, adapted to sequence processing SOMs, allow the detection of clusters also for real-valued sequence elements. (C) 2004 Elsevier B.V. All rights reserved.

Harvard Citation style: Strickert, M., Hammer, B. and Blohm, S. (2005) Unsupervised recursive sequence processing, Neurocomputing, 63, pp. 69-97. https://doi.org/10.1016/j.neucom.2004.01.190

APA Citation style: Strickert, M., Hammer, B., & Blohm, S. (2005). Unsupervised recursive sequence processing. Neurocomputing. 63, 69-97. https://doi.org/10.1016/j.neucom.2004.01.190