计算机科学 ›› 2020, Vol. 47 ›› Issue (10): 108-113.doi: 10.11896/jsjkx.190700112
周昌1,2, 李向利1,3, 李俏霖1, 朱丹丹1, 陈世莲1, 蒋丽榕1
ZHOU Chang1,2, LI Xiang-li1,3, LI Qiao-lin1, ZHU Dan-dan1, CHEN Shi-lian1, JIANG Li-rong1
摘要： 基本的非负矩阵分解应用于图像聚类时,对异常点的处理不够鲁棒,稀疏性较差。为了提高分解后的矩阵的稀疏性,在基本的非负矩阵分解算法中引入了L2,1范数,对基本的非负矩阵分解模型进行了改进,从而实现稀疏性,提升算法的性能。同时,为了降低各特征之间的关联,强化非负矩阵分解模型特征的独立性,引入了余弦相似度,提出了基于余弦相似度的稀疏非负矩阵分解算法。该算法在处理高维数据和提取特征方面具有显著优势,并且可提高算法在图像聚类中的辨别准确性。实验结果表明,所提算法在一系列评价指标上的效果优于传统的非负矩阵分解算法。
中图分类号:
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