Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome. (arXiv:2205.09906v1 [stat.ML])

Data augmentation plays a key role in modern machine learning pipelines.
While numerous augmentation strategies have been studied in the context of
computer vision and natural language processing, less is known for other data
modalities. Our work extends the success of data augmentation to compositional
data, i.e., simplex-valued data, which is of particular interest in the context
of the human microbiome. Drawing on key principles from compositional data
analysis, such as the Aitchison geometry of the simplex and subcompositions, we …

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