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Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms

Department of Cartographic and Terrain Engineering, Higher Polytechnic School of Ávila, University of Salamanca, Hornos Caleros 50, 05003 Ávila, Spain
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Appl. Sci. 2020, 10(24), 9133; https://doi.org/10.3390/app10249133
Received: 6 November 2020 / Revised: 17 December 2020 / Accepted: 19 December 2020 / Published: 21 December 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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. View Full-Text
Keywords: archaeological data science; artificial intelligence; unsupervised learning; generative adversarial networks; robust statistics archaeological data science; artificial intelligence; unsupervised learning; generative adversarial networks; robust statistics
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MDPI and ACS Style

Courtenay, L.A.; González-Aguilera, D. Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms. Appl. Sci. 2020, 10, 9133. https://doi.org/10.3390/app10249133

AMA Style

Courtenay LA, González-Aguilera D. Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms. Applied Sciences. 2020; 10(24):9133. https://doi.org/10.3390/app10249133

Chicago/Turabian Style

Courtenay, Lloyd A.; González-Aguilera, Diego. 2020. "Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms" Appl. Sci. 10, no. 24: 9133. https://doi.org/10.3390/app10249133

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