|本期目录/Table of Contents|

[1]叶秀斌*,简彩仁,夏靖波.两阶段最小二乘回归子空间聚类方法[J].厦门大学学报(自然科学版),2019,58(04):595-599.[doi:10.6043/j.issn.0438-0479.201902024]
 YE Xiubin*,JIAN Cairen,XIA Jingbo.Two-stage least square regression subspace clustering method[J].Journal of Xiamen University(Natural Science),2019,58(04):595-599.[doi:10.6043/j.issn.0438-0479.201902024]
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两阶段最小二乘回归子空间聚类方法(PDF)
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《厦门大学学报(自然科学版)》[ISSN:0438-0479/CN:35-1070/N]

卷:
58卷
期数:
2019年04期
页码:
595-599
栏目:
研究论文
出版日期:
2019-07-28

文章信息/Info

Title:
Two-stage least square regression subspace clustering method
文章编号:
0438-0479(2019)04-0595-05
作者:
叶秀斌*简彩仁夏靖波
厦门大学嘉庚学院信息科学与技术学院, 福建 漳州 363105
Author(s):
YE Xiubin*JIAN CairenXIA Jingbo
School of Information Science and Technology,Xiamen University Tan Kah Kee College,Zhangzhou 363105,China
关键词:
最小二乘回归 子空间聚类 局部约束 高维数据
Keywords:
least square regression subspace clustering local constraint high dimensional data
分类号:
TP 311; TP 371
DOI:
10.6043/j.issn.0438-0479.201902024
文献标志码:
A
摘要:
针对高维数据的非线性特性会降低最小二乘回归(LSR)子空间聚类的性能,提出两阶段LSR(TLSR)子空间聚类方法.该方法利用LSR的表示系数定义局部信息惩罚项,构造局部约束LSR方法.在8个数据集上的实验表明该方法适合高维数据的聚类.
Abstract:
Since the nonlinear characteristic of high-dimensional data reduced the performance of least square regression subspace clustering,a two-stage least square regression subspace clustering method is proposed.In the proposed method,the representation coefficient of least square regression to define local information is used.A local constrained least square regression method is constructed by using the local penalty term.Experiments on eight data sets show that the method is suitable for high-dimensional data clustering.

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备注/Memo

备注/Memo:
收稿日期:2019-02-28 录用日期:2019-05-09
基金项目:福建省自然科学基金(2018J01101); 福建省中青年教师教育科研项目(JT180799); 厦门大学嘉庚学院校级科研孵化项目(2018L04)
*通信作者:yexiubin@xujc.com
更新日期/Last Update: 1900-01-01