LVRF: A Latent Variable Based Approach for Exploring Geographic Datasets

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Liangdong Deng, Arpan Mahara, N. Rishe, Malek Adjouadi
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引用次数: 1

Abstract

Geographic datasets are usually accompanied by spatial non-stationarity – a phenomenon that the relationship between features varies across space. Naturally, nonstationarity can be interpreted as the underlying rule that decides how data are generated and alters over space. Therefore, traditional machine learning algorithms are not suitable for handling non-stationary geographic datasets, as they only render a single global model. To solve this problem, researchers often adopt the multiple-local-model approach, which uses different models to account for different sub-regions of space. This approach has been proven efficient but not optimal, as it is inherently difficult to decide the size of subregions. Additionally, the fact that local models are only trained on a subset of data also limits their potential. This paper proposes an entirely different strategy that interprets nonstationarity as a lack of data and addresses it by introducing latent variables to the original dataset. Backpropagation is then used to find the best values for these latent variables. Experiments show that this method is at least as efficient as multiple-local-model-based approaches and has even greater potential.
LVRF:一种基于潜变量的地理数据集挖掘方法
地理数据集通常伴随着空间非平稳性——一种特征之间的关系在空间上变化的现象。当然,非平稳性可以解释为决定数据如何生成和随空间变化的基本规则。因此,传统的机器学习算法不适合处理非平稳地理数据集,因为它们只呈现单一的全局模型。为了解决这一问题,研究人员通常采用多局部模型方法,即使用不同的模型来考虑空间的不同子区域。这种方法已被证明是有效的,但不是最佳的,因为确定分区域的大小本身就很困难。此外,局部模型只在数据子集上训练的事实也限制了它们的潜力。本文提出了一种完全不同的策略,将非平稳性解释为缺乏数据,并通过向原始数据集引入潜在变量来解决这个问题。然后使用反向传播来找到这些潜在变量的最佳值。实验表明,该方法至少与基于多局部模型的方法一样有效,并且具有更大的潜力。
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来源期刊
IPSI BgD Transactions on Internet Research
IPSI BgD Transactions on Internet Research COMPUTER SCIENCE, INFORMATION SYSTEMS-
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