计算机科学 ›› 2020, Vol. 47 ›› Issue (7): 66-70.doi: 10.11896/jsjkx.190600155
高方远1, 王秀美2
GAO Fang-yuan1, WANG Xiu-mei2
摘要： 聚类分析是机器学习与数据挖掘中的重要工具,而子空间聚类是高维数据分析中常用的聚类方法。基于谱图的子空间聚类方法首先学习数据在子空间中的自表示系数矩阵,然后基于此进行谱聚类分析。通过研究子空间聚类的过程和模型设计,发现基于子空间的聚类方法存在难以保持数据非线性和局部几何结构的问题。为此,文中提出了一种可以提取非线性结构的子空间聚类方法。首先,使用非线性映射函数将原始数据空间映射到高维的线性空间,利用块对角表示保持子空间的独立性。此外,为了对聚类过程中数据的局部结构进行约束,文中引入了基于拉普拉斯矩阵的流形正则项。然后,采用3种计算拉普拉斯矩阵的方法设计不同的基于流形正则和块对角约束的非线性子空间聚类模型。最后,在不同数据集上的实验结果验证了所提算法的有效性。
中图分类号:
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