基于局部回归融合的多核聚类方法  

Multiple Kernel Clustering via Local Regression Integration

作　　者：杜亮[1,2] 任鑫 张海莹 周芃 DU Liang;REN Xin;ZHANG Hai-ying;ZHOU Peng(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Institute of Big Data Science and Industry,Shanxi University,Taiyuan 030006,China;School of Computer Science and Technology,Anhui University,Hefei 230601,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]山西大学大数据科学与产业研究院,太原030006 [3]安徽大学计算机科学与技术学院,合肥230601

出　　处：《计算机科学》2021年第8期47-52,共6页Computer Science

基　　金：国家自然科学基金(61976129,61806003)。

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。Multiple kernel methods less consider the intrinsic manifold structure of multiple kernel data and estimate the consensus kernel matrix with quadratic number of variables,which makes it vulnerable to the noise and outliers within multiple candidate kernels.This paper first presents the clustering method via kernelized local regression(CKLR).It captures the local structure of kernel data and employs kernel regression on the local region to predict the clustering results.Moreover,this paper further extends it to perform clustering via the multiple kernel local regression(CMKLR).We construct the kernel level local regression sparse coefficient matrix for each candidate kernel,which well characterize the kernel level manifold structure.We then aggregate all the kernel level local regression coefficients via linear weights and generate the consensus sparse local regression coefficient,which largely reduces the number of candidate variables and becomes more robust against noises and outliers within multiple kernel data.Thus,the proposed method CMKLR avoids the above two limitations.It only contains one additional hyper parameter for turning.Extensive experimental results show that the clustering performance of the proposed method on benchmark data set is better than that of 10 state-of-the-art multiple kernel clustering methods.

关 键 词：多核聚类 局部回归 局部学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]