Algorithm for learning centre of single class based on k-means and semi-supervised mechanism
基于k-means和半监督机制的单类中心学习算法

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.