Abstract
Here we introduce VSOM, an efficient implementation of stochastic training for self-organizing maps. We derive VSOM from the standard stochastic training algorithm as published by Kohonen by replacing all iterative constructs in the algorithm with vector and matrix operations. Our novel implementation based on these vector and matrix operations provides substantial performance increases over Kohonen’s iterative algorithm as well as batchSOM, currently the fastest implementation of Self-organizing maps (SOM) training without resorting to multi-processing. The quality of the maps produced by VSOM matches the quality of the maps produced by the original iterative algorithm and outperforms the quality of the maps produced by batchSOM. In its current incarnation VSOM is single threaded and therefore well suited as a replacement for iterative stochastic training of self-organizing maps in R since R does not support multi-threading well.
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Hamel, L. (2019). VSOM: Efficient, Stochastic Self-organizing Map Training. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_60
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DOI: https://doi.org/10.1007/978-3-030-01057-7_60
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