基于k-means和半监督机制的单类中心学习算法

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。

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

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.