Feature maps, that preserve the global topology of arbitrary datasets, can be formed by self-organizing competing agents. So far, it has been presumed that global interaction of agents is necessary for this process. We establish that this is not the case, and that global topology can be uncovered through strictly local interactions. Enforcing uniformity of map quality across all agents results in an algorithm that is able to consistently uncover the global topology of diversely challenging datasets. The applicability and scalability of this approach is further tested on a large point cloud dataset, revealing a linear relation between map training time and size. The presented work not only reduces algorithmic complexity but also constitutes first step towards a distributed self organizing map.

中文翻译：

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通过在竞争性学习中进行本地互动进行全球协作。

可以通过自组织竞争代理来形成保留任意数据集的全局拓扑的特征图。到目前为止，已经假定代理程序的全局交互对于此过程是必需的。我们确定情况并非如此，并且可以通过严格的本地交互来发现全局拓扑。跨所有代理强制实施地图质量的统一性导致了一种算法，该算法能够始终如一地发现各种挑战性数据集的全局拓扑。此方法的适用性和可伸缩性在大的点云数据集上进行了进一步测试，揭示了地图训练时间与大小之间的线性关系。提出的工作不仅降低了算法复杂度，而且构成了迈向分布式自组织图的第一步。