摘要
核栽培者是聚类最近 Camastra 和 Verri 建议的方法的一个新奇的核。它证明为各种各样的数据的好性能关于流行聚类的算法有利地设定并且比较。然而，方法的主要缺点是在处理大数据集合的弱可伸缩能力，它极大地限制它的应用程序。在这份报纸，我们用核心集合建议一个可伸缩起来的核栽培者方法，它是比为聚类的大数据的原来的方法显著地快的。同时，它能处理很大的数据集合。象合成数据集合一样的基准数据集合的数字实验显示出建议方法的效率。方法也被用于真实图象分割说明它的性能。
Kernel grower is a novel kernel clustering method proposed recently by Camastra and Verri.It shows good performance for various data sets and compares favorably with respect to popular clustering algorithms.However,the main drawback of the method is the weak scaling ability in dealing with large data sets,which restricts its application greatly.In this paper,we propose a scaled-up kernel grower method using core-sets,which is significantly faster than the original method for large data clustering. Meanwhile,it can deal with very large data sets.Numerical experiments on benchmark data sets as well as synthetic data sets show the efficiency of the proposed method.The method is also applied to real image segmentation to illustrate its performance.
出处
《自动化学报》
EI
CSCD
北大核心
2008年第3期376-382,共7页
Acta Automatica Sinica
基金
Supported by National Natural Science Foundation of China(60675039)
National High Technology Research and Development Program of China(863 Program)(2006AA04Z217)
Hundred Talents Program of Chinese Academy of Sciences
关键词
大型数据集
图象分割
模式识别
磁心配置
核聚类
Kernel clustering
core-set
large data sets
image segmentation
pattern recognition
