Non-negative Matrix and Tensor Factorization Based Classification of Clinical Microarray Gene Expression Data

被引：0
|
作者：
Li, Yifeng [1 ]
Ngom, Alioune [1 ]
机构：
[1] Univ Windsor, Sch Comp Sci, Windsor, ON, Canada
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE | 2010年
基金：
加拿大自然科学与工程研究理事会;
关键词：
tensor decomposition; NMF; HONMF; GST data; CONSTRAINED LEAST-SQUARES; PREDICTION; TUMOR;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Non-negative information can benefit the analysis of microarray data. This paper investigates the classification performance of non-negative matrix factorization (NMF) over gene-sample data. We also extends it to higher-order version for classification of clinical time-series data represented by tensor. Experiments show that NMF and the higher-order NMF can achieve at least comparable prediction performance.
引用
收藏
页码：438 / 443
页数：6
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