| 基于二维最小二乘回归的子空间分割 |
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| 引用本文： | 刘展杰,陈晓云. 基于二维最小二乘回归的子空间分割[J]. 福州大学学报(自然科学版), 2016, 44(3): 431-436 |
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| 作者姓名： | 刘展杰 陈晓云 |
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| 作者单位： | 福州大学数学与计算机科学学院,福建,福州 350116,福州大学数学与计算机科学学院,福建,福州 350116 |
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| 基金项目： | 福建省自然科学基金资助项目(2014J01009) |
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| 摘 要： | 现实中有很多样本数据是二维的,且多数聚类方法需将二维样本数据向量化,从而导致二维数据的内部几何信息丢失.针对这一问题,提出二维最小二乘回归子空间分割方法直接对二维数据进行聚类,将一维最小二乘回归子空间分割方法推广到二维,使得原始数据的结构信息得以保留.在人脸数据集和哥伦比亚大学图像数据集上进行实验,结果表明该方法是有效的.
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| 关 键 词： | 聚类；最小二乘回归；子空间分割；二维样本 |
Two-dimensional least square regression based subspace segmentation |
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| LIU Zhanjie and CHEN Xiaoyun. Two-dimensional least square regression based subspace segmentation[J]. Journal of Fuzhou University(Natural Science Edition), 2016, 44(3): 431-436 |
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| Authors: | LIU Zhanjie and CHEN Xiaoyun |
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| Affiliation: | College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350116 and College of Mathematics and Computer Science, Fuzhou University, Fuzhou, Fujian 350116 |
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| Abstract: | In reality, most of data is two-dimensional, and most of clustering methods with vector treatment first. This practice leads to loss internal geometry information of data. To solve this problem, a two-dimensional least square regression method based subspace segmentation is put forward for clustering on 2-dimensional data. 1-dimensional space is extended to 2-dimensional space, and it keeps the structure information of original data. Experimental results show that this method is effective on face databases and the Columbia University Image Library. |
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| Keywords: | clustering least square regression subspace segmentation 2-dimensional space |
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