Projection subspace clustering

被引：6

作者：

Chen X. ^{[1
]}

Liao M. ^{[1
]}

Ye X. ^{[2
]}

机构：

[1] College of Mathematics and Computer Science, Fuzhou University, Fuzhou

[2] College of Economic and Management, Fuzhou University, Fuzhou

来源：

Ye, Xianbao (yexb5626@163.com) | 1600年 / SAGE Publications Inc.卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Gene expression data; Least-square regression; Low-rank representation; Projection; Sparse; Subspace clustering;

D O I：

10.1177/1748301817707321

中图分类号：

学科分类号：

摘要：

Gene expression data is a kind of high dimension and small sample size data. The clustering accuracy of conventional clustering techniques is lower on gene expression data due to its high dimension. Because some subspace segmentation approaches can be better applied in the high-dimensional space, three new subspace clustering models for gene expression data sets are proposed in this work. The proposed projection subspace clustering models have projection sparse subspace clustering, projection low-rank representation subspace clustering and projection least-squares regression subspace clustering which combine projection technique with sparse subspace clustering, low-rank representation and least-square regression, respectively. In order to compute the inner product in the high-dimensional space, the kernel function is used to the projection subspace clustering models. The experimental results on six gene expression data sets show these models are effective. © The Author(s) 2017.

引用

页码：224 / 233

页数：9

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