Review for NeurIPS paper: Simple and Scalable Sparse k-means Clustering via Feature Ranking

Summary and Contributions: This paper focuses on the problem of clustering in high dimension. K-means clustering is an extremely popular tool (especially in biomedical applications). However, as underlined by the authors, its performance is severely hindered in high-dimensional space --- leaving the data analyst no chance but to (a) apply some dimensionality reduction technique before performing the clustering or (b) selecting the features that are the most informative for the clustering and apply k-means on a subset of the features. This paper proposes a version of the later approach, choosing a sparse and interpretable subset of features. The setting is the following.