Beyond Worst-Case Coreset Bounds for $k$-Clustering via Determinantal Sampling
This work shows that significantly smaller coresets are possible under mild and natural assumptions on the underlying data distribution, and introduces a new correlated sampling framework, called \textit{determinantal sampling}, based on a novel application of determinantal point processes.
Diptarka Chakraborty, Satyaki Mukherjee, Gaurav Vallabhdas Revankar et al.
· 0 citations