Variance Reduced K-means Clustering

被引：0

作者：

Zhao, Yawei ^{[1
]}

Ming, Yuewei ^{[1
]}

Liu, Xinwang ^{[1
]}

Zhu, En ^{[1
]}

Yin, Jianping ^{[2
]}

机构：

[1] Natl Univ Def Technol, Coll Comp, Changsha 410073, Hunan, Peoples R China

[2] Dongguan Univ Technol, Dongguan 523000, Peoples R China

来源：

THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2018年

基金：

中国国家自然科学基金;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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-means VRKM, which outperforms the state-of-the-art method, and obtain 4x speedup for large-scale clustering.

引用

页码：8187 / 8188

页数：2

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