1. It is confusing to me what the exact goal of this paper is. Are we claiming the multi-prototype model is superior to other binary classification models (such as linear SVM, kNN, etc.) in terms of interpretability? Why do we have two sets of baselines for higher-dimensional and lower-dimensional data? 2. In Figure 3, for the baselines on the left hand side, what if we sparsify the trained models to reduce the number of selected features and compare accuracy to the proposed model? 3. Since the parameter for sparsity constraint has to be manually picked, can the authors provide any experimental results on the sensitivity of this parameter? Similar issue arises when picking the number of prototypes. Update after Author's Feedback: All my concerns are addressed by the authors's additional results. I'm changing my score based on that.
2. In Figure 3, for the baselines on the left hand side, what if we sparsify the trained models to reduce the number of selected features and compare accuracy to the proposed model?