JOINT CLUSTERING OF SPARSE NEIGHBORHOOD CONSTRAINTS BASED ON CLASS CONSISTENCY LEARNING
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摘要: 为了充分挖掘特征结构，提升聚类性能，提出一种基于类别一致性学习的稀疏邻域约束的联合聚类方法。将联合聚类问题转化为附加对偶正则化子的非负矩阵三因式分解，在非负矩阵分解的基础上，增加两个正则化子，使数据关联性与标签分配一致；提出一种目标优化的乘法交替方案，从理论上证明了算法的收敛性和正确性。利用三种评价方法在六个数据集上进行验证，并对其参数敏感性进行分析。实验结果表明，该算法具有较优的聚类性能。Abstract: In order to fully mine the feature structure and improve the clustering performance, a joint clustering method with sparse neighborhood constraints based on category consistency learning is proposed. The joint clustering problem was transformed into a tri-factorization of nonnegative matrix with dual regularizer. Based on the nonnegative matrix decomposition, two regularizers were added to make the data relevance consistent with the label assignment. A multiplication alternation scheme for objective optimization was introduced, and the convergence and correctness of the algorithm were proved theoretically. The three evaluation methods were verified on six data sets, and their parameter sensitivity was analyzed. Experiments show that the proposed algorithm has better performance.
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