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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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
Print ISBN: 978-3-319-12636-4
Online ISBN: 978-3-319-12637-1
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