块对角子空间聚类中成对约束的主动式学习

针对块对角表示(block diagonal representation, BDR)子空间聚类算法在对子空间重叠的高维数据聚类时效果较差的问题,提出成对约束的块对角子空间聚类(constrained subspace clustering with block diagonal representation, CBDR)算法,设计主动式学习策略,获取用户提供的少量数据点成对信息,以改进BDR算法的性能,给出CBDR算法的目标函数和求解过程。在测试集上的试验结果表明,CBDR算法的聚类错误率和归一化互信息指标比BDR和SBDR(structured block diagonal representation)算法好,而且主动式选取点对方法优于随机选取点对方法,使用少于5‰的约束信息可降低BDR的聚类错误率达到5%以上。

Active learning of pairwise constraints in block diagonal subspace clustering

Focusing on the poor performance of subspace clustering by block diagonal representation(BDR)on high-dimensional data with overlapped subspaces, an active learning strategy was designed to obtain partial pairwise information among a few data points. A pairwise constrained block diagonal representation algorithm(CBDR)was proposed to improve the performance of the BDR algorithm. The objective function and solution process of the CBDR were given. The experimental results on the test datasets showed that the CBDR algorithm reduced the clustering error by more than 5% with less than 5‰ constraint information in terms of clustering error and normalized mutual information, which significantly outperformed the compared algorithms, i.e., BDR, SBDR(structured block diagonal representation)with random selection of pairwise constraints.