arXiv:2511.04873v2 Announce Type: replace-cross Abstract: 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 interio
The continuous evolution of AI research pushes for more efficient and robust machine learning paradigms, with topological data analysis offering a novel approach to dataset compression and processing.
Advanced prototype selection methods, especially those leveraging topological structures, can significantly enhance the efficiency and interpretability of large AI models, reducing computational overhead and improving model generalization.
This research introduces a new class of prototype selection algorithms, demonstrating that considering the multi-scale topological structure of data can lead to more effective training set compression and potentially better model performance.
- · AI/ML researchers
- · Data scientists
- · Companies with large datasets
- · Hardware manufacturers (indirectly through efficiency gains)
- · Inefficient traditional data compression methods
- · Organizations slow to adopt advanced ML techniques
Improved efficiency in training AI models leads to faster development cycles and reduced computational costs.
More robust and generalizable AI models could emerge, capable of handling complex data with fewer instances.
The widespread adoption of topology-based methods might enable new AI applications that were previously too computationally intensive or unstable.
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Read at arXiv cs.LG