It is challenging to perform k-means clustering on a large scale dataset efficiently. One of the reasons is that k-means needs to scan a batch of training data to update the cluster centers at every iteration, which is time-consuming. In the paper, we propose a variance reduced k-mean VRKM, which outperforms the state-of-the-art method, and obtain 4× speedup for large-scale clustering. The source code is available on https://github.com/YaweiZhao/VRKM_sofia-ml.
Published Date: 2018-02-08
Registration: ISSN 2374-3468 (Online) ISSN 2159-5399 (Print)
Copyright: Published by AAAI Press, Palo Alto, California USA Copyright © 2018, Association for the Advancement of Artificial Intelligence All Rights Reserved.