半监督学习是近年来机器学习领域中的一个重要研究方向,其监督信息的质量对半监督聚类的结果影响很大,主动学习高质量的监督信息很有必要.提出一种纠错式主动学习成对约束的方法,该算法通过寻找聚类算法本身不能发现的成对约束监督信息,将其引入谱聚类算法,并利用该监督信息来调整谱聚类中点与点之间的距离矩阵.采用双向寻找的方法,将点与点间距离进行排序,使得学习器即使在接收到没有标记的数据时也能进行主动学习,实现了在较少的约束下可得到较好的聚类结果.同时,
该算法降低了计算复杂度,解决了聚类过程中成对约束的奇异问题.通过在UCI基准数据集以及人工数据集的实验表明,算法的性能好于相关对比算法,并优于采用随机选取监督信息的谱聚类性能.

Semi-suppervised learning is an important research direction in the field of machine learning in recent years. The performance of semi-supervised  lustering depends greatly on the quality of supervision information, so it is necessary to actively learn high quality supervision information. An active learning algorithm based on pair-wise constraints with error correction is proposed in this paper. The algorithm searches the pair-wise constraints information which clustering algorithm cannot find, and leads them into the spectral clustering algorithm. Utilizing suppervised information adjusts the distance matrix between points
in the spectral clustering, and sorts the distances by the two-way search method. The algorithm makes the learninger can study actively even the learinger receives the data without flags, and gets better clustering result with less constraints. Meanwhile, the algorithm reduces the computational complexity of the semi-supervised algorithms based on constraints and resolves the singular problem of the pair-wise constraints in the clustering process. Experimental
results on UCI benchmark data sets and artificial data set show that clearly the performance of the algorithm is better than that of other compared algorithms, and than that of the spectral clustering which randomly selects the supervision information.