Bidirectional compressive sensing for classification of gene expression data

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.