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首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Spherical self-organizing map using efficient indexed geodesic data structure.
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Spherical self-organizing map using efficient indexed geodesic data structure.

机译：使用有效的索引测地数据结构的球面自组织图。

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摘要

The two-dimensional (2D) Self-Organizing Map (SOM) has a well-known "border effect". Several spherical SOMs which use lattices of the tessellated icosahedron have been proposed to solve this problem. However, existing data structures for such SOMs are either not space efficient or are time consuming when searching the neighborhood. We introduce a 2D rectangular grid data structure to store the icosahedron-based geodesic dome. Vertices relationships are maintained by their positions in the data structure rather than by immediate neighbor pointers or an adjacency list. Increasing the number of neurons can be done efficiently because the overhead caused by pointer updates is reduced. Experiments show that the spherical SOM using our data structure, called a GeoSOM, runs with comparable speed to the conventional 2D SOM. The GeoSOM also reduces data distortion due to removal of the boundaries. Furthermore, we developed an interface to project the GeoSOM onto the 2D plane using a cartographic approach, whichgives users a global view of the spherical data map. Users can change the center of the 2D data map interactively. In the end, we compare the GeoSOM to the other spherical SOMs by space complexity and time complexity.
机译：二维（2D）自组织图（SOM）具有众所周知的“边界效应”。已经提出了几种使用棋盘形二十面体格子的球形SOM来解决该问题。但是，此类SOM的现有数据结构要么空间效率低下，要么在搜索邻居时很耗时。我们引入了二维矩形网格数据结构来存储基于二十面体的测地线圆顶。顶点关系由它们在数据结构中的位置来维护，而不是由直接邻居指针或邻接表维护。由于减少了指针更新引起的开销，因此可以有效地增加神经元的数量。实验表明，使用我们称为GeoSOM的数据结构的球形SOM以与传统2D SOM相当的速度运行。 GeoSOM还减少了由于去除边界而导致的数据失真。此外，我们开发了一个界面，可使用制图方法将GeoSOM投影到2D平面上，从而为用户提供球形数据图的全局视图。用户可以交互地更改2D数据图的中心。最后，我们通过空间复杂度和时间复杂度将GeoSOM与其他球形SOM进行了比较。

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