Two-sample test based on Self-Organizing Maps

• URL: http://arxiv.org/abs/2212.08960v1
• Date: Sat, 17 Dec 2022 21:35:47 GMT
• Title: Two-sample test based on Self-Organizing Maps
• Authors: Alejandro \'Alvarez-Ayll\'on, Manuel Palomo-Duarte, Juan-Manuel Dodero
• Abstract summary: Machine-learning classifiers can be leveraged as a two-sample statistical test. Self-Organizing Maps are a dimensionality reduction initially devised as a data visualization tool. But since their original purpose is visualization, they can also offer insights.
• Score: 68.8204255655161
• License: http://creativecommons.org/licenses/by/4.0/
• 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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