论文标题
随机自组织图
Randomized Self Organizing Map
论文作者
论文摘要
我们提出了自我组织图算法的变体，通过考虑在蓝噪声分布中随机放置神经元的随机位置，可以从中得出各种拓扑。这些拓扑具有随机的（但可控制的）不连续性，允许更加灵活的自组织，尤其是使用高维数据。提出的算法对单，二维和三维任务以及MNIST手写数字数据集进行了测试，并使用光谱分析和拓扑数据分析工具进行了验证。我们还展示了随机自组织图在神经病变和/或神经发生的情况下优雅地重组自身的能力。
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.
