Deterministic Feature Selection for $k$-means Clustering

We study feature selection for $k$-means clustering. Although the literature contains many methods with good empirical performance, algorithms with provable theoretical behavior have only recently been developed. Unfortunately, these algorithms are randomized and fail with, say, a constant probability. We address this issue by presenting a deterministic feature selection algorithm for k-means with theoretical guarantees. At the heart of our algorithm lies a deterministic method for decompositions of the identity.

💡 Research Summary

The paper addresses the problem of feature selection for k‑means clustering, focusing on the need for algorithms that provide provable guarantees without the failure probability inherent in existing randomized methods. While many practical feature‑selection techniques exist, only a few recent works (e.g., Boutsidis et al.