Abstract
In the online version of Self-Organizing Maps , the results obtained from different instances of the algorithm can be rather different. In this paper, we explore a novel approach which aggregates several results of the SOM algorithm to increase their quality and reduce the variability of the results. This approach uses the variability of the algorithm that is due to different initialization states. We use simulations to show that our result is efficient to improve the performance of a single SOM algorithm and to decrease the variability of the final solution. Comparison with existing methods for bagging SOMs also show competitive results.
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Notes
- 1.
The current fused map, \(\mathcal {M}^{*,b-1}\) has also been used as a reference map, with no difference in the final result. Using \(\mathcal {M}^1\) is thus a better strategy, because optimal transformation can be computed in parallel.
- 2.
http://cran.r-project.org/web/packages/sombrero, version 1.0.
- 3.
- 4.
- 5.
For the sake of paper length, detailed results are not reported but only described.
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Mariette, J., Villa-Vialaneix, N. (2016). Aggregating Self-Organizing Maps with Topology Preservation. In: Merényi, E., Mendenhall, M., O'Driscoll, P. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-319-28518-4_2
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DOI: https://doi.org/10.1007/978-3-319-28518-4_2
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