自动化学报2016，Vol.42Issue(8)：1238-1247,10.DOI:10.16383/j.aas.2016.c150335
局部子空间聚类
Local Subspace Clustering
摘要
Abstract
Existing subspace clustering methods usually rest on a global linear data set, which expresses each data point as a linear combination of all other data points, and thus common methods are not well suited for the nonlinear data. To overcome this limitation, the local sparse subspace clustering and local least squares regression subspace clustering are proposed. The idea of the two new methods comes from manifold learning which expresses each data point as a linear combination of its k nearest neighbors, and is combined with sparse subspace clustering and least squares subspace clustering respectively. Experimental results show that our method is effective on two-moon synthetic data, six image data sets and four gene expression data sets.关键词
局部线性/k 近邻/子空间聚类/图像数据/基因表达数据Key words
Local linear/k nearest neighbors/subspace clustering/image data/gene expression data引用本文复制引用
刘展杰,陈晓云..局部子空间聚类[J].自动化学报,2016,42(8):1238-1247,10.基金项目
Manuscript received May 29,2015 （）
accepted November 26,2015国家自然科学基金(71273053,11571074),福建省自然科学基金(2014J01009)资助 （71273053,11571074）
