Boosted unsupervised feature selection for tumor gene expression profiles

Abstract

AbstractIn an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples. Besides, it is difficult for these methods to guide the feature selection process by multiple feature subspaces comprehensively. In order to address these problems, a boosted unsupervised feature selection method (BoostUFS) is proposed for tumour gene expression profiles. Specifically, the authors design a boosting scheme to sequentially learn multiple compressed feature subspaces by focusing on ambiguous samples. The uncertainty of samples and the confidence of feature subspaces can be evaluated adaptively by minimising the overall loss of feature subspaces learning. Furthermore, we provide a consensus objective function with L2,1‐norm regularisation to combine these weighted feature subspaces and select discriminative features. Extensive experiments on several real‐world datasets of tumour gene expression profiles are carried out to demonstrate the effectiveness of the proposed method.