Premium
Bidirectional compressive sensing for classification of gene expression data
Author(s) -
Xu Xiaohua,
Fan Baichuan,
He Ping,
Liang Yali,
Ding Jie,
Lou Yuan,
Zhang Zhijun,
Chang Xincheng
Publication year - 2018
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5120
Subject(s) - compressed sensing , computer science , representation (politics) , sparse approximation , expression (computer science) , data mining , artificial intelligence , pattern recognition (psychology) , machine learning , politics , political science , law , programming language
Summary The classification of gene expression data is significantly important for medical diagnosis. In recent years, compressive sensing emerges as a popular sparse learning method and has been applied in different areas. It is featured with the sparse representation of data with a few atoms in the dictionary. However, the traditional compressive sensing model only focuses on the relationship among different samples but neglects the relationship among different genes. In order to take into account of the both kinds of correlation, we propose a novel bidirectional compressive sensing model for the classification of gene expression data. Under this model, we develop a novel Bi‐ADMM algorithm with three different variants to solve the optimization problem. The promising experimental results on the real‐world gene expression datasets demonstrate both the effectiveness and efficiency of our proposed approach.