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arXiv Machine Learning By Jordan Eckert, Henry Schenck

Skeletal Prototypes on Iterative Nerve Expansions

Read the original on arXiv Machine Learning →

The paper introduces Skeletal Prototypes on Iterative Nerve Expansions (SPINE), a prototype reduction method that represents each class as an embedded 1‑complex rather than a finite set of points. SPINE constructs its initial edge set from a class‑conditional Mapper graph, then refines vertex positions under a classification objective, allowing observations to be assigned to the nearest complex. Evaluated on seventeen benchmark datasets with stratified 10‑fold cross‑validation, SPINE achieves the highest mean accuracy and best average rank among seven competing methods, showing significant improvements over five of them and competitive performance across varying prototype budgets.

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