A multi-view ℓ1-norm SVM algorithm for data integration in biological applications

摘要

A multi-view ℓ-norm Support Vector Machine (SVM) for integrating data from different views to improve binary classification performance in a given view is proposed. The performance of the proposed algorithm is evaluated by integrating biological data from two different gene expression measurement technologies. The experimental results show that the data integration method proposed leads to a better classification performance in comparison to the traditional ℓ-norm SVM.