| 基于核最小二乘回归子空间分割的高维小样本数据聚类 |
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| 引用本文： | 简彩仁,翁 谦,陈晓云.基于核最小二乘回归子空间分割的高维小样本数据聚类[J].福州大学学报(自然科学版),2018,46(1):38-44. |
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| 作者姓名： | 简彩仁 翁 谦 陈晓云 |
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| 作者单位： | 厦门大学嘉庚学院,福州大学数学与计算机科学学院,福州大学数学与计算机科学学院 |
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| 基金项目： | 国家自然科学基金资助（No. 71273053），福建省教育厅中青年教师教育科研立项（No.JAT160087） |
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| 摘 要： | 针对基于线性表示理论的子空间分割方法没有考虑高维小样本数据的非线性性质,借鉴核理论,提出核最小二乘回归子空间分割方法,使子空间分割方法适合高维小样本数据的非线性性质.经6个基因表达数据集和4个图像数据集上的实验,表明该方法是有效的.
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| 关 键 词： | 最小二乘回归，子空间分割，核理论，聚类，高维小样本 |
| 收稿时间： | 2016/11/30 0:00:00 |
| 修稿时间： | 2016/12/27 0:00:00 |
High dimension small sample data clustering using kernel least square regression subspace segmentation |
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| JIAN Cairen,WENG Qian and CHEN Xiaoyun.High dimension small sample data clustering using kernel least square regression subspace segmentation[J].Journal of Fuzhou University(Natural Science Edition),2018,46(1):38-44. |
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| Authors: | JIAN Cairen WENG Qian and CHEN Xiaoyun |
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| Institution: | Tan Kah kee Colleage, Xiamen University,College of Mathematics and Computer Science,Fuzhou University,College of Mathematics and Computer Science,Fuzhou University |
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| Abstract: | High dimension small sample data exist widely in real life, and traditional clustering methods are not suitable for the nonlinear properties of high dimension small sample data. The classical subspace segmentation methods based on linear representation theory do not consider the nonlinear properties of high dimension small sample data. In sight of the kernel theory, the kernel least square regression subspace segmentation method is proposed to make the subspace segmentation method suitable for the nonlinear properties of high dimension small sample data. Experiments on six gene expression datasets and four image datasets show that the method is effective. |
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| Keywords: | least square regression subspace segmentation kernel theory clustering high dimension small sample |
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