Suppr超能文献

使用高效索引测地线数据结构的球形自组织映射。

Spherical self-organizing map using efficient indexed geodesic data structure.

作者信息

Wu Yingxin, Takatsuka Masahiro

机构信息

School of Information Technologies, The University of Sydney, Australia.

出版信息

Neural Netw. 2006 Jul-Aug;19(6-7):900-10. doi: 10.1016/j.neunet.2006.05.021. Epub 2006 Jun 19.

Abstract

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, which gives 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.

摘要

二维(2D)自组织映射(SOM)存在众所周知的“边界效应”。为了解决这个问题,人们提出了几种使用细分二十面体晶格的球形SOM。然而,此类SOM的现有数据结构要么空间效率不高,要么在搜索邻域时耗时。我们引入一种二维矩形网格数据结构来存储基于二十面体的测地线穹顶。顶点关系通过它们在数据结构中的位置来维护,而不是通过直接邻居指针或邻接表。由于减少了指针更新带来的开销,增加神经元数量可以高效完成。实验表明,使用我们的数据结构(称为GeoSOM)的球形SOM运行速度与传统二维SOM相当。GeoSOM还减少了由于去除边界而导致的数据失真。此外,我们开发了一个接口,使用制图方法将GeoSOM投影到二维平面上,这为用户提供了球形数据图的全局视图。用户可以交互式地更改二维数据图的中心。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进行比较。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验