基于log鲁棒核岭回归的子空间聚类算法

doi:10.6040/j.issn.1672-3961.0.2022.284

摘要/Abstract

摘要： 最小二乘回归子空间聚类算法存在对数据中噪声敏感、模型对数据结构信息约束不充分、没有考虑数据非线性关系等问题。针对这些问题,提出一种基于log函数的改进算法。使用L-(2,log)范数代替Frobenius范数约束残差项,提高算法的鲁棒性;使用logdet范数代替Frobenius范数约束表达矩阵,加强表达矩阵的低秩性;利用核方法处理数据,增强算法对数据非线性关系的捕捉能力,进而提高聚类的准确率。分别在人脸、手写数字、物体3种类别的数据集上与多个经典聚类算法进行对比试验,试验结果表明,该算法在精准度、标准化互信息、纯度3个聚类评价指标上优于对比算法,具有良好的聚类效果。

关键词: 子空间聚类, 谱聚类, 最小二乘回归, 核方法, 范数

中图分类号:

TP181

张鑫,费可可. 基于log鲁棒核岭回归的子空间聚类算法[J]. 山东大学学报 (工学版), 2023, 53(6): 26-34.

ZHANG Xin, FEI Keke. Log-based robust kernel ridge regression for subspace clustering[J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 26-34.

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