Growing Hierarchical Principal Components Analysis Self-Organizing Map

  • Stones Lei Zhang
  • Zhang Yi
  • Jian Cheng Lv
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3971)


In this paper, we propose a new self-growing hierarchical principal components analysis self-organizing neural networks model. This dynamically growing model expands the ability of the PCASOM model that represents the hierarchical structure of the input data. It overcomes the shortcoming of the PCASOM model in which the fixed the network architecture must be defined prior to training. Experiment results showed that the proposed model has better performance in the tradition clustering problem.


Covariance Matrix Vector Basis Training Process Machine Learn Database Orthonormal Vector Basis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Lopez-Rubio, E., Munoz-Perez, J., Gomez-Ruiz, J.A.: A Principal Components Analysis Self-Organizing Map. Neural Networks 17, 261–270 (2004)MATHCrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Emergence of Invariant-Feature Dectors in the Adaptive-Subspace SOM. Biological Cybernetics 75, 281–291 (1996)MATHCrossRefGoogle Scholar
  3. 3.
    Kohonen, T.: The Self-Organizing Map. Proc. IEEE 78, 1464–1480 (1990)CrossRefGoogle Scholar
  4. 4.
    Blackmore, J., Miikkulainen, R.: Incremental Grid Growing: Encoding High-Dimensional Structure into a Two-Dimensional Feature Map. In: Proc. IEEE Int. Conf. Neural Networks, San Francisco, CA, vol. 1, pp. 450–455 (1993)Google Scholar
  5. 5.
    Fritzke, B.: Growing Grid A Self-Organizing Network with Constant Neighborhood Range and Adaption Strength. Neural Processing Letter 2, 1–5 (1995)Google Scholar
  6. 6.
    Alahakoon, D., Halgamuge, S.K., Srinivasan, B.: Dynamic Self-Organizing Maps with Controlled Growth for Knowledge Discovery. IEEE Trans. Neural Networks 11, 601–614 (2000)CrossRefGoogle Scholar
  7. 7.
    Rauber, A., Merkl, D., Dittenbach, M.: The Growing Hierarchical Self-organizing Map: Exploratory Analysis of High-Dimensional Data, pp. 1331–1341. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  8. 8.
    Moreno, S., Allende, H., Rogel, C., Salas, R.: Robust Growing Hierarchical Self-Organizing Map. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 341–348. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Pampalk, E., Widmer, G., Chan, A.: A New Approach to Hierarchical Clustering and Structuring of Data with Self-Oranizing Maps. Intell. Data Analysis 8, 131–149 (2004)Google Scholar
  10. 10.
    Murphy, P.M.: UCI Repository of Machine Learning Database and Domain Theories[online], Data of access: March 2001, Available,

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Stones Lei Zhang
    • 1
  • Zhang Yi
    • 1
  • Jian Cheng Lv
    • 1
  1. 1.Computational Intelligence Laboratory, School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China

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