基于L1范数的k平面聚类算法设计
TP391; 基于L2范数度量的k平面聚类(k-Plane Clustering,kPC)设计思想,本文提出了一种采用L1范数度量的聚类算法.由于在平面更新步骤中,所导出的优化问题是非凸的,文中给出了一种求解方法,即将非凸问题转化为有限个子集上的凸问题,为避免求解多个优化问题导致训练时间过长问题,本文还设计了一种新的优选策略,有限个子集的搜索任务可在线性时间内完成.本文所提出的方法只需要求解k个线性规划,而不再是kPC的求解特征值问题.在人工和UCI数据集上的实验结果表明:基于L1范数平面聚类算法的训练和测试时间更短,且在大多数数据集上均表现出了更好的聚类性能....
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Published in | 南京航空航天大学学报 Vol. 51; no. 5; pp. 681 - 686 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | Chinese |
Published |
南京林业大学信息科学技术学院,南京,210037%国家林业局调查规划设计院,北京,100714
01.10.2019
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Subjects | |
Online Access | Get full text |
ISSN | 1005-2615 |
DOI | 10.16356/j.1005-2615.2019.05.014 |
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Abstract | TP391; 基于L2范数度量的k平面聚类(k-Plane Clustering,kPC)设计思想,本文提出了一种采用L1范数度量的聚类算法.由于在平面更新步骤中,所导出的优化问题是非凸的,文中给出了一种求解方法,即将非凸问题转化为有限个子集上的凸问题,为避免求解多个优化问题导致训练时间过长问题,本文还设计了一种新的优选策略,有限个子集的搜索任务可在线性时间内完成.本文所提出的方法只需要求解k个线性规划,而不再是kPC的求解特征值问题.在人工和UCI数据集上的实验结果表明:基于L1范数平面聚类算法的训练和测试时间更短,且在大多数数据集上均表现出了更好的聚类性能. |
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AbstractList | TP391; 基于L2范数度量的k平面聚类(k-Plane Clustering,kPC)设计思想,本文提出了一种采用L1范数度量的聚类算法.由于在平面更新步骤中,所导出的优化问题是非凸的,文中给出了一种求解方法,即将非凸问题转化为有限个子集上的凸问题,为避免求解多个优化问题导致训练时间过长问题,本文还设计了一种新的优选策略,有限个子集的搜索任务可在线性时间内完成.本文所提出的方法只需要求解k个线性规划,而不再是kPC的求解特征值问题.在人工和UCI数据集上的实验结果表明:基于L1范数平面聚类算法的训练和测试时间更短,且在大多数数据集上均表现出了更好的聚类性能. |
Author | 杨绪兵 许等平 业巧林 寇振宇 杨红鑫 张福全 |
AuthorAffiliation | 南京林业大学信息科学技术学院,南京,210037%国家林业局调查规划设计院,北京,100714 |
AuthorAffiliation_xml | – name: 南京林业大学信息科学技术学院,南京,210037%国家林业局调查规划设计院,北京,100714 |
Author_FL | YANG Hongxin KOU Zhenyu YANG Xubing ZHANG Fuquan YE Qiaolin XU Dengping |
Author_FL_xml | – sequence: 1 fullname: YANG Hongxin – sequence: 2 fullname: YANG Xubing – sequence: 3 fullname: KOU Zhenyu – sequence: 4 fullname: YE Qiaolin – sequence: 5 fullname: ZHANG Fuquan – sequence: 6 fullname: XU Dengping |
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DocumentTitle_FL | k Plane Clustering Algorithm Based on L1 Norm |
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Keywords | L1范数 线性规划 凸问题 平面聚类 |
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