Scalable k-Means Clustering via Lightweight Coresets
Olivier Bachem (ETH Zurich); Mario Lucic (Google); Andreas Krause (ETH Zurich)
Coresets are compact representations of data sets such that models trained on a coreset are provably competitive with models trained on the full data set. As such, they have been successfully used to scale up clustering models to massive data sets. While existing approaches generally only allow for multiplicative approximation errors, we propose a novel notion of lightweight coresets that allows for both multiplicative and additive errors. We provide a single algorithm to construct lightweight coresets for k -means clustering as well as soft and hard Bregman clustering. The algorithm is substantially faster than existing constructions, embarrassingly parallel, and the resulting coresets are smaller. We further show that the proposed approach naturally generalizes to statistical k -means clustering and that, compared to existing results, it can be used to compute smaller summaries for empirical risk minimization. In extensive experiments, we demonstrate that the proposed algorithm outperforms existing data summarization strategies in practice.
How can we assist you?
We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!
If you are experiencing any issue related to registrations (confirmation, payment problem etc.) or have any questions regarding registrations, please do not submit this form. Please send an email to Kelly Hughes (email@example.com) or call 1.888.526.1242 or 303.530.4683.
Please enter the word you see in the image below: