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Journal Articles Neural Computation Year : 2021

Randomized Self Organizing Map


We propose a variation of the self organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess random (but controllable) discontinuities that allow for a more flexible self-organization, especially with highdimensional data. The proposed algorithm is tested on one-, two-and three-dimensions tasks as well as on the MNIST handwritten digits dataset and validated using spectral analysis and topological data analysis tools. We also demonstrate the ability of the randomized self-organizing map to gracefully reorganize itself in case of neural lesion and/or neurogenesis.
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hal-03017448 , version 1 (20-11-2020)



Nicolas P. Rougier, Georgios Is Detorakis. Randomized Self Organizing Map. Neural Computation, inPress, ⟨10.1162/neco_a_01406⟩. ⟨hal-03017448⟩
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