Compositional Data

Compositional data analysis focuses on data representing proportions or relative abundances, such as those found in microbiome studies or natural language processing. Current research emphasizes developing models and algorithms that effectively handle the inherent constraints of compositional data, including techniques like Aitchison geometry, probabilistic circuits, and various deep learning architectures (e.g., transformers, variational autoencoders) tailored for compositional structures. This field is crucial for advancing understanding in diverse areas, from improving the interpretability and robustness of machine learning models to enabling more accurate analyses of complex biological systems and materials science data.