Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real-World Data

TL;DR

This paper explores the interpretability and representability of topological data analysis, spectral theory, and algebraic methods in mathematical AI, demonstrating their applications and comparative advantages on diverse real-world datasets.

Contribution

It systematically analyzes three foundational mathematical AI methods—persistent homology, persistent Laplacians, and persistent commutative algebra—and compares their interpretability and effectiveness.

Findings

PH captures topological invariants efficiently.

PL incorporates spectral information for geometric sensitivity.

PCA provides rich algebraic and combinatorial insights.

Abstract

Recent years have witnessed a fast growth in mathematical artificial intelligence (AI). One of the most successful mathematical AI approaches is topological data analysis (TDA) via persistent homology (PH) that provides explainable AI (xAI) by extracting multiscale structural features from complex datasets. This work investigates the interpretability and representability of three foundational mathematical AI methods, PH, persistent Laplacians (PL) derived from spectral theory, and persistent commutative algebra (PCA) rooted in Stanley-Reisner theory. We apply these methods to a set of data, including geometric shapes, synthetic complexes, fullerene structures, and biomolecular systems to examine their geometric, topological and algebraic properties. PH captures topological invariants such as connected components, loops, and voids through persistence barcodes. PL extends PH by incorporating spectral information, quantifying topological invariants, geometric stiffness and connectivity via harmonic and non-harmonic spectra. PCA introduces algebraic invariants such as graded Betti numbers, facet persistence, and f/h-vectors, offering combinatorial, topological, geometric, and algebraic perspectives on data over scales. Comparative analysis reveals that while PH offers computational efficiency and intuitive visualization, PL provides enhanced geometric sensitivity, and PCA delivers rich algebraic interpretability. Together, these methods form a hierarchy of mathematical representations, enabling explainable and generalizable AI for real-world data.