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A self-organizing method for map reconstruction

机译:一种自组织的地图重建方法

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A variety of problems in geographical and satellite-based remote sensing signal processing, and in the area of "zero-error" pattern recognition dealing with processing the information contained in the distances between the points in the geographical or feature space. In this paper we consider one such problem, namely, that of reconstructing the points in the geographical or feature space, when we are only given the approximate distances between the points themselves. In particular, we are interested in the problem of reconstructing a map when the given data is the set of intercity road travel distances. Reported solution approaches primarily involve multi-dimensional scaling techniques. However, we propose a self-organizing method. The new method is tested and compared with the classical multi-dimensional scaling and ALSCAL on different data sets obtained from various countries.
机译:在基于地理和基于卫星的遥感信号处理中,以及在“零误差”模式识别领域中,涉及处理地理或特征空间中点之间距离中包含的信息的各种问题。在本文中,我们仅考虑点之间的近似距离,便会考虑这样一个问题,即在地理或特征空间中重建点。特别是,当给定数据是城市间道路行驶距离的集合时,我们对重构地图的问题感兴趣。报告的解决方案方法主要涉及多维缩放技术。但是,我们提出了一种自组织方法。在从不同国家获得的不同数据集上,对新方法进行了测试,并与经典的多维标度和ALSCAL进行了比较。

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