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analyzing-adversarial-node-placement-in-decentralized-federated-learning-networks

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スペクトルアルゴリズムの一般化誤差について

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都市シーンの意味的占有マップを通じた拡散


PCA outcomes in geometric morphometrics are artefacts of the input data and are neither reliable, robust, nor reproducible. Supervised machine learning classifiers and outlier detection methods outperform PCA in accurately classifying samples and detecting new taxa.


coremsg

Evolutionary biologists, primarily anatomists and ontogenists, employ modern geometric morphometrics to quantitatively analyse physical forms (e.g., skull morphology) and explore relationships, variations, and differences between samples and taxa using landmark coordinates. The standard approach comprises two steps, Generalised Procrustes Analysis (GPA) followed by Principal Component Analysis (PCA). PCA projects the superimposed data produced by GPA onto a set of uncorrelated variables, which can be visualised on scatterplots and used to draw phenetic, evolutionary, and ontogenetic conclusions. Recently, the use of PCA in genetic studies has been challenged. Due to PCA’s central role in morphometrics, we sought to evaluate the standard approach and claims based on PCA outcomes. To test PCA’s accuracy, robustness, and reproducibility using benchmark data of the crania of five papionin genera, we developed MORPHIX, a Python package containing the necessary tools for processing superimposed landmark data with classifier and outlier detection methods, which can be further visualised using various plots. We discuss the case of Homo Nesher Ramla , an archaic human with a questionable taxonomy. We found that PCA outcomes are artefacts of the input data and are neither reliable, robust, nor reproducible as field members may assume and that supervised machine learning classifiers are more accurate both for classification and detecting new taxa. Our findings raise concerns about PCA-based findings in 18,000 to 32,900 studies. Our work can be used to evaluate prior and novel claims concerning the origins and relatedness of inter- and intra-species and improve phylogenetic and taxonomic reconstructions.

### Competing Interest Statement

The authors have declared no competing interest.

MORPHIX: Resolving sample identification bias in morphometrics analysis with a supervised machine learning package

limitations-of-principal-component-analysis-in-geometric-morphometrics-a-supervised-machine-learning-approach-for-accurate-classification-and-outlier-detection


Limitations of Principal Component Analysis in Geometric Morphometrics: A Supervised Machine Learning Approach for Accurate Classification and Outlier Detection


title_rewrite

Evaluating the reliability and accuracy of Principal Component Analysis (PCA) in geometric morphometrics analysis