计算机应用与软件2017，Vol.34Issue(8)：251-256,6.DOI:10.3969/j.issn.1000-386x.2017.08.045
基于改进非负矩阵分解的肿瘤基因表达谱特征提取
FEATURE EXTRACTION OF TUMOR GENE EXPRESSION PROFILES BASED ON IMPROVED NON-NEGATIVE MATRIX FACTORIZATION
摘要
Abstract
According to the characteristics of the tumor gene expression profiles, we proposed a feature extraction algorithm, based on low-rank graph non-negative matrix factorization (LGNMF).It solved the lack of information on the global structure data of NMF algorithm and promoted the validity of feature extraction.The algorithm had improved the description of local and global data structures, based on NMF algorithm with low-rank graph constraints, which made feature space have stronger classification ability after feature extraction.The low-dimensional feature space was obtained by LGNMF algorithm, and it was classified by KNN classifier.We compared with the NMF, GNMF and RGNMF algorithm in four groups of standard tumor gene expression profile data sets.The experimental results show that LGNMF algorithm can improve the effect on classification.关键词
低秩图/特征空间/肿瘤基因表达谱/特征提取Key words
Low-rank graph/Feature space/Tumor gene expression profile/Feature extraction分类
信息技术与安全科学引用本文复制引用
黄经纬,杨国亮,王艳芳,胡政伟..基于改进非负矩阵分解的肿瘤基因表达谱特征提取[J].计算机应用与软件,2017,34(8):251-256,6.基金项目
国家自然科学基金项目(51365017,61305019) （51365017,61305019）
江西省教育厅科技计划项目(GJJ150680). （GJJ150680）
