Bagging Voronoi classifiers for clustering spatial functional data
Bagging Voronoi classifiers for clustering spatial functional data
复制标题
用于聚类空间功能数据的 Bagging Voronoi 分类器
DOI：
10.1016/j.jag.2012.03.006
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
Valeria Vitelli
中科院分区：
文献类型：
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作者：
P. Secchi;S. Vantini;Valeria Vitelli
我们提出了一种基于随机Voronoi镶嵌的装袋策略，用于探索地理参考功能数据，适用于不同的目的（例如，分类、回归、降维等）。敦促由表面太阳能数据库中包含的环境数据的应用程序，我们特别关注的问题，聚类功能数据索引的网站的空间有限格。因此，我们说明了我们的战略，通过实施一个具体的算法，其基本原理是（i）取代原始数据集与减少，由当地代表的邻域覆盖整个调查区域;（ii）分析当地代表;（iii）对于与随机生成的不同邻域集合相关联的不同缩减数据集合，多次重复先前的分析，从而获得分析的许多不同的弱公式;（iv）最后，将弱分析打包在一起以获得结论性的强分析。通过广泛的模拟研究，我们表明，这种新的程序-它不需要一个明确的模型的空间依赖性-是统计和计算效率。
We propose a bagging strategy based on random Voronoi tessellations for the exploration of geo-referenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, …). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure – which does not require an explicit model for spatial dependence – is statistically and computationally efficient.