2025/11/06 by Eckert, Jordan, Ceyhan, Elvan, Schenck, Henry
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Cell Image Analysis Techniques #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Morphological variations and asymmetry #Topological and Geometric Data Analysis
paper · doi:10.48550/arxiv.2511.04873
openalex publication_date 2025/11/06 · openalex created_date 2025/11/11 · openalex updated_date 2026/07/28
Prototype selection methods compress a training set, but the existing taxonomy of condensation, edition, hybrid, competence-based, optimization-based, and clustering-based families does not include methods that operate on the multi-scale topological structure of the data. This paper introduces two different persistence-based prototype selector variants, Topological Prototype Selector (TPS) and Boundary-Conscious Topological Prototype Selector (BoundaryTPS). TPS uses two sequential Rips filtrations to retain boundary-relevant and interior-typical points. BoundaryTPS is a single-stage variant whose vertex-weighted filtration concentrates retention near the decision boundary. We evaluate both methods against seven classical baselines on fifteen real datasets and find that the topological methods occupy a different operating point in the prototype-selection design space than existing methods. BoundaryTPS achieves the lowest mean Friedman rank on H1 persistence-diagram preservation and is significantly better than five of the seven baselines (Nemenyi, α= 0.05). TPS ranks third on the same endpoint. Both methods are more stable under fold perturbation than any chained-decision selector tested, and both inherit the source set's class proportions without label-aware machinery. On aggregate G-Mean both methods are competitive but not leading, with rank-1 frequencies of 11.3% (TPS) and 9.9% (BoundaryTPS) across fold combinations. Empirically, both methods scale sub-quadratically in sample size.