Abstract
We propose Merge Growing Neural Gas (MGNG) as a novel unsupervised growing neural network for time series analysis. MGNG combines the state-of-the-art recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In order to focus on frequent sequence patterns an entropy maximization strategy is utilized which controls the creation of new neurons. Experimental results demonstrate reduced time complexity compared to MNG while retaining similar accuracy in time series representation.
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References
Strickert, M., Hammer, B.: Merge SOM for temporal data. Neurocomputing 64, 39–71 (2005)
Fritzke, B.: A Growing Neural Gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) NIPS, pp. 625–632. MIT Press, Cambridge (1995)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
Martinetz, T., Martinetz, T., Berkovich, S., Schulten, K.: “Neural-gas” Network for Vector Quantization and its Application to Time-Series Prediction. IEEE-Transactions on Neural Networks 4(4), 558–569 (1993)
Carpinteiro, O.A.S.: A Hierarchical Self-Organizing Map Model for Sequence Recognition. In: Proc. of ICANN, vol. 2, pp. 815–820. Springer, London (1998)
Hammer, B., Villmann, T.: Classification using non standard metrics. In: Proc. of ESANN, pp. 303–316 (2005)
Euliano, N.R., Principe, J.C.: A Spatio-Temporal Memory Based on SOMs with Activity Diffusion. In: Oja (ed.) Kohonen Maps, pp. 253–266. Elsevier, Amsterdam (1999)
Hammer, B., Micheli, A., Sperduti, A., Strickert, M.: Recursive self-organizing network models. Neural Networks 17(8-9), 1061–1085 (2004)
Hammer, B., Micheli, A., Neubauer, N., Sperduti, A., Strickert, M.: Self Organizing Maps for Time Series. In: Proc. of WSOM, Paris, France, pp. 115–122 (2005)
Hammer, B., Micheli, A., Sperduti, A.: A general framework for unsupervised processing of structured data. In: Proc. of ESANN, vol. 10, pp. 389–394 (2002)
Chappell, G.J., Taylor, J.G.: The Temporal Kohonen map. Neural Networks 6(3), 441–445 (1993)
Koskela, T., Varsta, M., Heikkonen, J., Kaski, K.: Temporal sequence processing using Recurrent SOM. In: Proc. of KES, pp. 290–297. IEEE, Los Alamitos (1998)
Voegtlin, T.: Recursive Self-Organizing Maps. Neural Networks 15(8-9) (2002)
Hagenbuchner, M., Sperduti, A., Tsoi, A.C.: A Self-Organizing Map for adaptive processing of structured data. Neural Networks 14(3), 491–505 (2003)
Lambrinos, D., Scheier, C., Pfeifer, R.: Unsupervised Classification of Sensory-Motor states in a Real World Artifact using a Temporal Kohonen Map. In: Proc. of ICANN, vol. 2, EC2, pp. 467–472 (1995)
Farkas, I., Crocker, M.: Recurrent networks and natural language: exploiting self-organization. In: Proc. of CogSci (2006)
Farka, I., Crocker, M.W.: Systematicity in sentence processing with a recursive Self-Organizing Neural Network. In: Proc. of ESANN (2007)
Trentini, F., Hagenbuchner, M., Sperduti, A., Scarselli, F., Tsoi, A.: A Self-Organising Map approach for clustering of XML documents. In: WCCI (2006)
Estevez, P.A., Zilleruelo-Ramos, R., Hernandez, R., Causa, L., Held, C.M.: Sleep Spindle Detection by Using Merge Neural Gas. In: WSOM (2007)
Martinetz, T.: Competitive Hebbian Learning Rule Forms Perfectly Topology Preserving Maps. In: Proc. of ICANN, pp. 427–434. Springer, Heidelberg (1993)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997)
Kyan, M., Guan, L.: Local variance driven self-organization for unsupervised clustering. In: Proc. of ICPR, vol. 3, pp. 421–424 (2006)
Fritzke, B.: Vektorbasierte Neuronale Netze. PhD thesis, Uni Erlangen (1998)
Strickert, M., Hammer, B., Blohm, S.: Unsupervised recursive sequence processing. Neurocomputing 63, 69–97 (2005)
Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inf. Process. Lett. 31(1), 7–15 (1989)
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Andreakis, A., Hoyningen-Huene, N.v., Beetz, M. (2009). Incremental Unsupervised Time Series Analysis Using Merge Growing Neural Gas. In: Príncipe, J.C., Miikkulainen, R. (eds) Advances in Self-Organizing Maps. WSOM 2009. Lecture Notes in Computer Science, vol 5629. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02397-2_2
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DOI: https://doi.org/10.1007/978-3-642-02397-2_2
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