Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Geometric Morphometric Data Augmentation using Generative Computational Learning Algorithms

Version 1 : Received: 26 November 2020 / Approved: 27 November 2020 / Online: 27 November 2020 (14:43:36 CET)

A peer-reviewed article of this Preprint also exists.

Courtenay, L.A.; González-Aguilera, D. Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms. Appl. Sci. 2020, 10, 9133. Courtenay, L.A.; González-Aguilera, D. Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms. Appl. Sci. 2020, 10, 9133.

Journal reference: Appl. Sci. 2020, 10, 9133
DOI: 10.3390/app10249133

Abstract

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization.

Subject Areas

Archaeological Data Science; Artificial Intelligence; Unsupervised Learning; Generative Adversarial Networks; Robust Statistics.

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