计算机科学 ›› 2021, Vol. 48 ›› Issue (8): 47-52.doi: 10.11896/jsjkx.201000106
杜亮1,2, 任鑫1, 张海莹1, 周芃3
DU Liang1,2, REN Xin1, ZHANG Hai-ying1, ZHOU Peng3
摘要: 针对现有多核聚类方法较少考虑多核数据局部流形结构以及在多核融合时学习参数过多进而易受多核噪声异常等干扰的问题,文中首先提出了基于局部核回归的聚类方法(CKLR)。该方法通过局部学习来刻画单核数据的流形结构并采用稀疏化的局部核回归系数来进行预测和聚类。文中进一步提出了基于单核局部核回归融合的多核聚类方法(CMKLR)。该方法为每个核矩阵构造对应的稀疏化的局部核回归系数,并采用全局线性加权融合的方式获得了多核数据下的局部流形结构和同样稀疏化的多核局部回归系数。所提方法较好地避免了现有方法的两个缺陷,且该方法仅包含局部邻域大小这一超参数。实验结果表明,所提方法在测试数据集上的聚类性能优于当前的主流多核聚类方法。
中图分类号:
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