ABSTRACT

Sparse functional data are commonly observed in real-data analyzes. For such data, we propose a new classification method based on functional principal component analysis (FPCA) and bootstrap aggregating. Bootstrap aggregating is believed to improve the single classifier. In this paper, we apply this belief to an FPCA based classification, and compare the classification performance with that of the single classifiers. The simulation results show that the proposed method performs better than the conventional single classifiers. We then conduct two real-data analyzes.

中文翻译：

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稀疏函数数据的 Bootstrap 聚合分类

摘要

在实际数据分析中通常会观察到稀疏功能数据。对于此类数据，我们提出了一种基于功能主成分分析（FPCA）和引导聚合的新分类方法。Bootstrap 聚合被认为可以改进单个分类器。在本文中，我们将此信念应用于基于 FPCA 的分类，并将分类性能与单个分类器的分类性能进行比较。仿真结果表明，所提出的方法比传统的单一分类器表现更好。然后我们进行两次真实数据分析。