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arXiv · 2212.08960

Two-sample test based on Self-Organizing Maps

Alejandro Álvarez-Ayllón·Manuel Palomo-Duarte·Juan-Manuel Dodero
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Abstract

Machine-learning classifiers can be leveraged as a two-sample statistical test. Suppose each sample is assigned a different label and that a classifier can obtain a better-than-chance result discriminating them. In this case, we can infer that both samples originate from different populations. However, many types of models, such as neural networks, behave as a black-box for the user: they can reject that both samples originate from the same population, but they do not offer insight into how both samples differ. Self-Organizing Maps are a dimensionality reduction initially devised as a data visualization tool that displays emergent properties, being also useful for classification tasks. Since they can be used as classifiers, they can be used also as a two-sample statistical test. But since their original purpose is visualization, they can also offer insights.

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Alejandro Álvarez-Ayllón, Manuel Palomo-Duarte, Juan-Manuel Dodero. 2022-12-17. Two-sample test based on Self-Organizing Maps. https://arxiv.org/abs/2212.08960

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