Lee Daewon, Lee Jaewook
IEEE Trans Neural Netw. 2007 Mar;18(2):578-83. doi: 10.1109/TNN.2006.889495.
A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data.
提出了一种用于半监督分类的新型学习算法。该方法首先构建一个支持函数，该函数使用标记数据和未标记数据来估计数据分布的支持度。然后，借助动力系统将整个数据空间划分为少量不相交的区域。最后，利用标记数据和由构建的支持函数描述的聚类结构对分解后的区域进行标记。仿真结果表明了该方法对样本外未标记测试数据以及样本内未标记数据进行标记的有效性。
