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Paper 2011.09534 paper by Nicolas P. Rougier
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Paper 2011.09534

by Nicolas P. Rougier arxiv-paper--2011.09534
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0.0 Top 18%
S: Semantic 50
A: Authority 0
P: Popularity 0
R: Recency 0
Q: Quality 0
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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 high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensions tasks as well as on the MNIST handwritten digits data...

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BibTeX 
@misc{arxiv_paper__2011.09534,
  author = {Nicolas P. Rougier},
  title = {Paper 2011.09534 Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2011.09534v1}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Nicolas P. Rougier. (2026). Paper 2011.09534 [Paper]. Free2AITools. https://arxiv.org/abs/2011.09534v1

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📝 Executive Summary

"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 high-dimensional data. The proposed algorithm is tested on one-, two- and three-dimensions tasks as well as on the MNIST handwritten digits data..."

❝ Cite Node 

@article{Rougier2020ArXiv,
  title={ArXiv 2011.09534 Technical Profile},
  author={Nicolas P. Rougier and Georgios Is. Detorakis},
  journal={arXiv preprint arXiv:arxiv-paper--2011.09534},
  year={2020}
}

đŸ‘Ĩ Collaborating Minds

Nicolas P. Rougier Georgios Is. Detorakis

Abstract & Analysis

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 high-dimensional 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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🆔 Identity & Source

id
arxiv-paper--2011.09534
author
Nicolas P. Rougier
tags
arxiv:cs.NEarxiv:cs.LGgan

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