Variance Reduced K-Means Clustering

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.

Authors

• Yawei Zhao
• Yuewei Ming
• Xinwang Liu National University of Defense Technology
• En Zhu National University of Defense Technology
• Jianping Yin

Keywords

No keywords are indexed for this paper.