1)Less Novelty: The algorithm for construction of coresets itself is not novel. Existing coreset frameworks for classical k-means and (k,z) clusterings are extended to the kernelized setting. 2)Clarity: Since the coreset construction algorithm is built up on previous works, a reader without the background in literature on coresets would find it hard to understand why the particular sampling probabilities are chosen and why they give particular guarantees. It would be useful rewrite the algorithm preview and to give at least a bit of intuition on how the importance sampling scores are chosen and how they can give the coreset guarantees Suggestions:
In the experiment section, other than uniform sampling, it would be interesting to use some other classical k-means coreset as baselines for comparison.
Please highlight the technical challenges and contributions clearly when compared to coresets for classical k-means.
1)Less Novelty: The algorithm for construction of coresets itself is not novel. Existing coreset frameworks for classical k-means and (k,z) clusterings are extended to the kernelized setting.