%0 Journal Article
%T 基于二维最小二乘回归的子空间分割<br>Two-dimensional least square regression based subspace segmentation
%A 刘展杰
%A 陈晓云
%J 福州大学学报(自然科学版)
%D 2016
%R 10.7631/issn.1000-2243.2016.03.0431
%X 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.<br>In reality，most of data is two-dimensional，and most of clustering methods process the data with vectorization 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
%K 聚类 最小二乘回归 子空间分割 二维样本&lt
%K br&gt
%K clustering least square regression subspace segmentation 2-dimensional space
%U http://xbzrb.fzu.edu.cn/ch/reader/view_abstract.aspx?file_no=201603025&flag=1