This approach trades the flexibility of learned models for interpretability and formal guarantees: you get provable error bounds and confidence scores for each prediction, but performance lags behind neural baselines on some datasets due to limited descriptor expressiveness.
PLACE is a method for classifying point clouds and graphs using topological features (persistent homology) with mathematical guarantees.