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基于骨形状数据,对原始人类灵长类动物标本的分类进行主要成分和线性分辨分析

Marie J M Vanhoof¹, Balder Croquet^2,3, Isabelle De Groote^4,5

¹Department of Development & Regeneration, Biomedical Sciences Group, KU Leuven Campus Kulak, Kortrijk, Belgium.

Royal Society open science

|September 22, 2023

概括

机器学习准确地使用triquetrum骨形状来对灵长类动物进行分类. 这种工具揭示了活着的灵长类动物和化石人类动物之间的进化关系,有助于分类学和行为解释.

科学领域:

古人类学古人类学.
灵长类动物形态学
机器学习应用机器学习应用

背景情况:

了解灵长类动物进化和化石原始人之间的关系至关重要.
三骨的骨形状为区分灵长类群体提供了潜在的可能性.
以前的方法缺乏对比形态学的定量准确性.

研究的目的:

为了测试机器学习来根据三形状对现存灵长类动物进行分类.
应用这个分类工具来推断现存灵长类动物和化石人类动物之间的亲缘关系.
在此分类任务中,评估主要组件分析 (PCA) 和线性差别分析 (LDA) 的准确性.

主要方法:

使用无监督 (PCA) 和监督 (LDA) 机器学习管道.
从现存灵长类动物的三骨中提取形状数据.
他对现存的灵长类动物进行了分类,并随后将该模型应用于化石原始人样本.
采用PCA和统一的多重近似和投影 (UMAP) 来进行数据可视化.

主要成果:

在使用PCA和LDA对现存灵长类动物标本进行分类时,获得了0.90的高F1得分.
机器学习模型准确地反映了已知的分类学和化石人类的运动行为.
证明了三形状分析在区分灵长类群体方面的有效性.

结论:

机器学习应用于三形状数据,为现存灵长类动物提供了高度准确的分类工具.
这种方法量化地比较化石和现存灵长类动物形态,支持分类学差异化.
这种方法有助于解释化石遗迹和理解进化亲和关系.

关键词:

在 LDA LDA 中.在PCA中,PCA是PCA.卡尔帕骨是指手掌的部分.逻辑回归的逻辑回归方法形态学形态学形态学

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