计算机科学 ›› 2023, Vol. 50 ›› Issue (7): 89-97.doi: 10.11896/jsjkx.220500050
刘威, 邓秀勤, 刘冬冬, 刘玉兰
LIU Wei, DENG Xiuqin, LIU Dongdong, LIU Yulan
摘要: 现有的基于对称非负矩阵因式分解(Symmetric Nonnegative matrix Factorization,SymNMF)算法大都仅依赖初始数据构造亲和矩阵,并且一定程度上忽视了样本有限的成对约束信息,无法有效区分不同类别的相似样本以及学习样本的几何特征。针对以上问题,提出了基于约束图正则的块稀疏对称非负矩阵分解(Block Sparse Symmetric Nonnegative Matrix Factorization Based on Constrained Graph Regularization,CGBS-SymNMF)。首先,通过先验信息构造约束图矩阵,用于指导类别指示矩阵区分高相似度的不同类别样本;然后,引入PCP-SDP(Pairwise Constraint Propagation by Semi-definite Programming)方法,利用成对约束学习一个新的样本图映射矩阵;最后,利用“勿连”约束构造不相似矩阵,用于引导一个块稀疏正则项,以增强模型抗噪能力。实验结果表明,所提算法具有更高的聚类精确度和稳定性。
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