基于k-means和半监督机制的单类中心学习算法  

Algorithm for learning centre of single class based on k-means and semi-supervised mechanism

作　　者：李志圣[1] 孙越恒[1] 何丕廉[1] 侯越先[1] 

机构地区：[1]天津大学计算机科学与技术学院,天津300072

出　　处：《计算机应用》2008年第10期2513-2516,共4页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60603027);天津市应用基础研究计划项目(05YFJMJC11700)

摘　　要：提出了一个基于k-means算法框架和半监督机制的single-means算法,以解决单类中心学习问题。k-means算法实质上是对一种混合高斯模型的期望最大化(EM)算法的近似,对该模型随机生成的多类混合数据集,从目标类中随机标定的初始中心出发,能确定地收敛到该类的实际中心。将single-means算法应用到对单类文本中心学习问题中,实验结果表明:在给定目标类中的小标定文本集后,新算法能够有效地改进类的初始中心,且对数据稀疏和方差较大的实际问题具有健壮性。A new algorithm named ＂single-means＂ was presented to improve the centre estimation of the object class when a hybrid data set had unknown k value and feature of accumulating to centre. Based on that k-means algorithm was equivalent to Expectation Maximum （EM） algorithm on a special hybrid Gaussian model, it was proved that given a data set generated by the above Gaussian model, the true centre of the object Gaussian distribution could be converged by a new algorithm. The new algorithm was applied in learning the centre of single text class. The experiment shows that given a small labeled text set, the new algorithm can get a better centre, and is robust on sparse data set and that with great variance.

关 键 词：K-MEANS 单类学习 半监督学习 single—means 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]