Categorical and geometric methods in statistical, manifold, and machine learning
arXiv:2505.03862v1h-index: 15
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This work presents incremental applications of existing theoretical frameworks to various learning domains.
The paper applies categorical and geometric methods, specifically the category of probabilistic morphisms, to problems in statistical, manifold, and machine learning, as part of a broader forthcoming book.
We present and discuss applications of the category of probabilistic morphisms, initially developed in \cite{Le2023}, as well as some geometric methods to several classes of problems in statistical, machine and manifold learning which shall be, along with many other topics, considered in depth in the forthcoming book \cite{LMPT2024}.