计算机应用与软件2024，Vol.41Issue(12)：324-333,10.DOI:10.3969/j.issn.1000-386x.2024.12.045
基于类别一致性学习的稀疏邻域约束的联合聚类
JOINT CLUSTERING OF SPARSE NEIGHBORHOOD CONSTRAINTS BASED ON CLASS CONSISTENCY LEARNING
摘要
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.关键词
联合聚类/稀疏邻域约束/非负矩阵分解/一致性学习Key words
Joint clustering/Sparse neighborhood constraint/Nonnegative matrix decomposition/Consistent learning分类
信息技术与安全科学引用本文复制引用
蒋超,许堉坤,张芮嘉,安佰龙..基于类别一致性学习的稀疏邻域约束的联合聚类[J].计算机应用与软件,2024,41(12):324-333,10.基金项目
国网上海市电力公司项目(SGSHJY00GPJS1800310). （SGSHJY00GPJS1800310）
