Abstract
Vector quantization principles have efficiently been employed in number of clustering and classification algorithms due to its ability to estimate the probability density function of a multivariate stationary data distribution. However, application of the same principles to the algorithms which cater for non-stationary data spaces pose a massive challenge to maintain the quantization quality of learning outcomes. In order to maintain and improve the quantization quality in non-stationary data spaces, this paper presents an enhancement to an existing learning model. From experiments it has been proved that the enhanced learning model significantly improves the quantization error compared to its original version. Furthermore, the modification has resulted a less memory consuming and computationally more efficient algorithm.
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References
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 147, 59–69 (1982)
Hamker, F.H.: Life-long learning Cell Structures–continuously learning without catastrophic interference. Neural Networks 14(4-5), 551–573 (2001)
Shen, F., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Networks 19(1), 90–106 (2006)
Shen, F., Ogura, T., Hasegawa, O.: An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks 20(8), 893–903 (2007)
De Silva, D., Alahakoon, D.: Incremental knowledge acquisition and self learning from text. In: International Joint Conference on Neural Networks, pp. 1–8 (2010)
Deng, D., Kasabov, N.: ESOM: An algorithm to evolve self-organizing maps from online data streams. In: Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 6, pp. 3–8 (2000)
Shah-Hosseini, H., Safabakhsh, R.: TASOM: A new time adaptive self-organizing map. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 33(2), 271–282 (2003)
Gunawardana, K., Rajapakse, J., Alahakoon, D.: Brain-inspired self-organizing model for incremental learning. In: International Joint Conference on Neural Networks, pp. 1–8 (2013)
Squire, L., Berg, D., Bloom, F.E., du Lac, S., Ghosh, A., Spitzer, N.C.: Fundamental Neuroscience, 3rd edn., pp. 87–270. Academic Press (2008)
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Gunawardana, K., Rajapakse, J., Alahakoon, D. (2014). Improving Quantization Quality in Brain-Inspired Self-organization for Non-stationary Data Spaces. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8834. Springer, Cham. https://doi.org/10.1007/978-3-319-12637-1_65
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DOI: https://doi.org/10.1007/978-3-319-12637-1_65
Publisher Name: Springer, Cham
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