计算机科学 ›› 2021, Vol. 48 ›› Issue (6): 86-95.doi: 10.11896/jsjkx.200800180
王中元, 刘惊雷
WANG Zhong-yuan, LIU Jing-lei
摘要: 高维数据集的处理是计算机视觉领域的核心,子空间聚类是实现高维数据聚类使用最广泛的方法之一。传统的子空间聚类假定数据来自不同的线性子空间,且不同子空间的区域不重叠。然而,现实中的数据往往不满足这两个约束条件,使得子空间聚类的效果受到影响。为了解决这两个问题,引入核化子空间来解决子空间数据的非线性问题,引入子空间系数矩阵的二阶近邻来处理重叠的子空间问题。随后,设计了基于二阶近邻的核化子空间三步聚类算法,首先求取核化子空间数据的自相似系数,然后消除子空间的重叠区域,最后对系数矩阵进行谱聚类。将所设计的子空间聚类算法首先在人工数据集上进行了测试,随后在人脸、场景字符和生物医学3类数据集中共12个真实数据集上进行了实验。实验结果表明,所提算法相比最新的几种算法具有一定的优势。
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